234 Data engineering Jobs in India

About LH2 AI LabsLH2 AI Labs is an applied research lab solving data platform and curation challenges for foundation model development. We serve every frontier AI lab with the mission of delivering the best data to power the best models.

Our customers are the ones building the foundation models themselves and our work sits directly in the loop of how those systems improve. This is a rare opportunity to join a company at a defining moment in AI.

About the role:

This is a full-time, hands-on role where you will own the core infrastructure and systems that enable us to discover customers' data landscape, handle sensitive data like names, address securely and to clean and catalog them into training-ready datasets.

That path covers connectors and on-prem agentic discovery components, secrets and PII scrubbing, scale and cost efficient ingestion and clearance for use in model training. You lead a small team of 3-4 engineers and raise the quality bar in your pod.

What you'll own

• Connectors and extraction - You'll build connectors for common SaaS and databases (Google Workspace, Slack, Postgres/MySQL, Jira, MongoDB) and for specific tools such as Razorpay, GreytHR, Keka, Zoho and LeadSquared. The rule is to build only where no good open-source connector or clean export exists.
• On-prem scanning - You'll build a CLI or agent that runs at the data owner's end to sample and estimate the value of their data without shipping all of it to us.
• Clearance pipeline - Secrets detection across full git history, fail-closed. PII redaction for code and conversational data, including Indian identifiers (PAN, Aadhaar, GSTIN, IFSC, UPI). Measurable recall and precision.
• Lineage - Every output record must trace back to its raw source, and every lot must be revocable.
• Delivery - Packaging, sampling for buyers, and supporting the supply and BD teams on data questions.

You have

• 8+ years in backend or data engineering, with at least 2 years leading up to 4 or more engineers.
• You've built ingestion or ETL pipelines that run in production.
• Hands-on work with sensitive data such as PII, financial or health data, including redaction, masking or access controls.
• The judgment to decide what to build and what to adopt.

Nice to have

• Experience with Presidio, gitleaks/TruffleHog, dlt or Airbyte.
• Knowledge of DPDP, GDPR, HIPAA or SOC 2.
• You've shipped software that runs in customers' environments.

This is not a pure people-management role. You'll write code, review everything, and personally own the hardest problems in the pod.

We are hiring a GCP Data Engineer to build scalable data pipelines on Google Cloud.

Responsibilities

• Build batch and streaming pipelines with Dataflow and Pub/Sub
• Model and optimise data warehouses in BigQuery
• Run large-scale processing on Dataproc
• Orchestrate workflows with Cloud Composer

Requirements

• 2+ years of data engineering on Google Cloud
• Hands-on with BigQuery and Dataflow
• Strong data modelling and SQL optimisation skills

We are looking for a Lead Data Architect to design, build, and scale our data pipelines and entity resolution systems. This role combines deep technical expertise in data engineering with hands-on experience in AI-assisted tooling, entity matching, and data integration from diverse sources. You will lead architectural decisions for our data platform, mentor engineers, and ensure our pipelines are reliable, scalable, and production-grade.

Key Responsibilities

     Architect, build, and maintain robust, scalable data pipelines that ingest, transform, and serve data from multiple internal and external sources.

     Own the end-to-end orchestration of data workflows using tools like Dagster, ensuring observability, reliability, and maintainability of pipelines.

     Design and implement entity resolution workflows — including matching, merging, and survivorship logic — using tools such as Splink, to produce clean, deduplicated, golden records.

     Build and maintain web scrapers to source data from external providers, ensuring resilience to source changes, rate limits, and data quality issues.

     Integrate and reconcile data coming from multiple, often inconsistent, sources into unified, trustworthy datasets.

     Design and maintain data models and schemas across transactional and analytical systems, ensuring consistency, scalability, and performance.

     Leverage AI/LLM-based tools and techniques to enhance data pipeline capabilities — e.g., intelligent data extraction, automated data quality checks, or AI-assisted entity matching.

     Define and enforce best practices around pipeline design, testing, monitoring, and documentation.

     Collaborate closely with data engineers, product managers, and other stakeholders to translate business requirements into scalable data architecture.

     Provide technical leadership and mentorship to the data engineering team.

Required Skills & Experience

     Strong hands-on experience building and maintaining production-grade data pipelines at scale.

     Practical experience with Dagster (or similar orchestration tools like Airflow/Prefect) for pipeline orchestration.

     Experience with Splink or similar probabilistic/deterministic record linkage tools for entity matching, merging, and survivorship.

     Strong proficiency in Python, including experience writing and maintaining web scrapers.

     Proven experience integrating and maintaining data pipelines that pull from multiple, heterogeneous data sources.

     Experience applying AI/ML tools within data engineering workflows (e.g., LLM-assisted data cleaning, extraction, or matching).

     Hands-on experience with relational and distributed databases such as PostgreSQL and Google Cloud Spanner.

     Strong understanding of data modeling principles (normalization, dimensional modeling, schema design) across OLTP and OLAP systems.

     Experience with cloud data warehousing platforms such as BigQuery, Redshift, and cloud platforms (GCP/AWS/Azure).

     Strong communication skills and experience working cross-functionally with engineering and product teams.

     Experience with distributed data processing frameworks (e.g., Spark, Dask).

     Familiarity with data governance, lineage, and cataloging tools.

     Prior experience in a lead or architect-level role guiding a data engineering team.

What We're Looking For

A technically strong, hands-on leader who can balance architectural thinking with the practical grit of debugging a flaky scraper or tuning a matching algorithm — someone who's comfortable owning both the big picture and the messy details of real-world data.

Next

Title                                 : Senior Data & AI Quality Engineer (Ingestion · Evaluation · Testing)

Experience                    : 5+ years

Work type                      : Chennai - Work from Office/ Other Locations - Remote

Employment Type      : Full Time

Notice Period               : Immediate

Work Day                       :Mon to Fri

Key Responsibilities:

• Ingestion pipelines: Confluence, SharePoint/Microsoft 365 (Graph), and repository connectors — parsing, chunking, metadata, incremental sync, data-quality controls
• Synthetic and non-production data design: corpora shaped to banking IT content (runbooks, incidents, KB articles, change records) with realistic permission structures — the foundation of the cloud-first build
• The evaluation harness as a product: golden question sets, retrieval precision/faithfulness/citation-accuracy scoring, the zero-leakage permission suite, the zero-unauthorized-actions agent suite; CI-integrated regression gates
• Performance and load testing with the platform engineer: concurrency profiles, soak tests, degraded-mode behavior
• UAT orchestration and defect triage across both use cases; acceptance evidence packs per milestone
• Measurement reporting foundations: the before/after value metrics (time saved per incident, per search) the client's executives receive monthly

Technical Skills:

• 5+ years across data engineering and quality engineering with production Python; you have built pipelines AND the tests that police them
• LLM evaluation experience: has designed or operated retrieval/generation quality measurement with numeric thresholds (RAGAS-class metrics, custom harnesses, or equivalent) — not just eyeballed outputs
• Document-processing depth: parsing real enterprise content (tables, permissions, versions, mess), chunking trade-offs, metadata design

• Test-suite craftsmanship: negative and adversarial test design — the leakage suite is a security artifact, and you think like an attacker when writing it
• Load/performance testing experience (Locust, k6, or equivalent) and defect-triage discipline

Strongly Preferred :

Synthetic-data generation for regulated domains; Microsoft Graph and Confluence APIs; Milvus/pgvector; Splunk data onboarding; UAT facilitation with business users; banking data-handling standards

About Ampera: 

Ampera Technologies, a purpose driven Digital IT Services with primary focus on supporting our client with their Data, AI / ML, Accessibility and other Digital IT needs. We also ensure that equal opportunities are provided to Persons with Disabilities Talent. Ampera Technologies has its Global Headquarters in Chicago, USA and its Global Delivery Center is based out of Chennai, India. We are actively expanding our Tech Delivery team in Chennai and across India. We offer exciting benefits for our teams, such as 1) Hybrid and Remote work options available, 2) Opportunity to work directly with our Global Enterprise Clients, 3) Opportunity to learn and implement evolving Technologies, 4) Comprehensive healthcare, and 5) Conducive environment for Persons with Disability Talent meeting Physical and Digital Accessibility standards. 

About the Role

We are looking for a high-energy, consultative, and commercially driven Account Executive – Microsoft Azure to accelerate Comprinno's Azure business.

This is a new-business acquisition and revenue-growth role. The Account Executive will identify, develop, qualify, and close Azure-led opportunities across mid-market, digital-native, and enterprise customers. The role will work closely with Microsoft, Comprinno's Partnerships team, Presales, Cloud Engineering, Data & AI, Managed Services, Marketing, and executive leadership.

The ideal candidate should understand enterprise technology sales and be capable of engaging CIOs, CTOs, Engineering leaders, Infrastructure leaders, Data/AI leaders, and business stakeholders around transformation initiatives.

This is not a transactional cloud-reselling role. We are looking for someone capable of developing consultative, multi-service customer relationships.

Role Charter

The Account Executive owns:

Prospect → Discover → Qualify → Shape → Propose → Negotiate → Close → Handover

The primary objective is:

Acquire new Azure customers and maximize the strategic value of every new customer relationship.

The Account Executive owns the commercial opportunity and closure.

Presales and Engineering own deep technical solutioning.

Customer Success owns post-sale retention and expansion following successful handover.

Key Responsibilities

1. Azure Business Development

• Build and execute a territory and account strategy for Comprinno's Azure business.
• Identify new-logo opportunities across target customer segments.
• Develop opportunities through:
• Direct prospecting
• Marketing-generated leads
• Microsoft referrals and co-sell
• Partner ecosystem
• Events and industry networks
• Existing executive relationships
• Build a healthy pipeline capable of consistently achieving revenue targets.
• Identify organizations undergoing cloud adoption, migration, modernization, Data/AI transformation, or infrastructure optimization.
• Develop multi-threaded relationships within target accounts.

2. Consultative Enterprise Selling

Engage customers around business and technology outcomes rather than individual Azure services.

Understand:

• Business objectives
• Existing technology landscape
• Cloud strategy
• Application portfolio
• Infrastructure challenges
• Security requirements
• Data & AI priorities
• Cost challenges
• Availability and resilience requirements
• Modernization roadmap

Translate those conversations into qualified opportunities for Comprinno.

Build relationships with decision-makers and influencers across CIO, CTO, CISO, Engineering, Infrastructure, Cloud, Data/AI, Procurement, and Finance functions.

3. Azure Solution Sales

Develop opportunities across Comprinno's Azure portfolio, including:

• Azure Cloud Migration
• Application Modernization
• Database Modernization
• Cloud-Native Engineering
• DevOps & Platform Engineering
• Kubernetes & Containerization
• Azure Managed Services
• Cloud Security & Compliance
• FinOps / Cost Optimization
• Disaster Recovery & Business Continuity
• Data Engineering & Analytics
• AI / Generative AI
• Agentic AI
• Cloud Consulting & Assessments

The AE is not expected to be a Solution Architect, but must possess sufficient technical understanding to conduct credible discovery conversations and identify when specialist technical involvement is required.

4. Microsoft Co-Sell & Ecosystem Engagement

• Build strong working relationships with relevant Microsoft field sellers, partner teams, solution specialists, and account teams.
• Identify joint account opportunities and participate in account mapping.
• Develop Microsoft-sourced and Microsoft-influenced pipeline.
• Work closely with Comprinno's Strategic Partnerships & Ecosystem team on joint GTM initiatives.
• Leverage appropriate Microsoft partner programs, marketplace opportunities, incentives, and funding mechanisms.
• Participate in joint customer meetings, workshops, events, and campaigns.
• Maintain disciplined follow-up on partner-generated opportunities.

The objective is to turn the Microsoft relationship into a repeatable customer-acquisition channel, not simply maintain partner engagement.

5. Opportunity Qualification

Apply disciplined qualification before significant technical and organizational resources are committed.

Establish:

• Customer problem
• Business impact
• Technical need
• Decision criteria
• Budget/commercial viability
• Decision-making stakeholders
• Competition
• Procurement process
• Expected timeline
• Next actions

Disqualify weak opportunities early and focus resources on opportunities with meaningful closure potential.

6. Opportunity Shaping & Solution Development

• Bring Presales and Engineering teams into qualified opportunities at the appropriate stage.
• Coordinate customer discovery workshops.
• Ensure technical teams clearly understand customer objectives and commercial context.
• Help shape solutions around business outcomes rather than technology alone.
• Coordinate assessments, workshops, POCs, proposals, and solution presentations.
• Ensure proposed solutions align with customer expectations, commercial realities, and Comprinno's delivery capabilities.

7. Commercial Ownership & Closure

Own the commercial lifecycle through closure.

• Develop proposals and commercial strategy.
• Coordinate pricing with internal stakeholders.
• Lead commercial negotiations.
• Navigate customer procurement processes.
• Coordinate SOWs, contracts, NDAs, and commercial approvals.
• Maintain momentum across complex buying cycles.
• Identify and resolve barriers to closure.
• Ensure opportunities have clear next actions and owners.

The AE remains accountable until the business is commercially closed, rather than treating proposal submission as the end of the sales process.

8. Pipeline & Forecast Management

• Maintain accurate opportunity information in HubSpot.
• Build sufficient pipeline coverage against quota.
• Maintain realistic close dates and opportunity stages.
• Conduct regular pipeline reviews.
• Forecast revenue accurately.
• Identify stalled opportunities and develop recovery strategies.
• Maintain clear next steps for every active opportunity.
• Ensure HubSpot remains the single source of truth for sales activity.

9. Land-and-Expand Strategy

The initial Azure engagement should be viewed as the beginning of a broader customer relationship.

Identify potential customer lifecycle paths such as:

Migration → Modernization → Managed Services → Security → Resilience → Data & AI

or

Data/AI → Cloud Modernization → Platform Engineering → Managed Services

The AE should establish the broader account potential during the acquisition process and capture it as part of the account strategy.

Following successful onboarding, expansion ownership transitions to the Customer Success organization.

10. Sales-to-Customer-Success Handover

• Ensure structured handover after customer acquisition.
• Transfer customer context, stakeholders, commitments, commercial terms, risks, and identified future opportunities.
• Introduce the Account Manager – Customer Success as the long-term commercial relationship owner.
• Remain engaged during the transition where executive or relationship continuity is required.

The objective is to create a seamless:

AE → Delivery → Customer Success

customer experience.

Required Qualifications & Skills

• Bachelor's degree in Engineering, Technology, Business, Management, or related discipline.
• 5–8 years of B2B technology sales experience, preferably in Cloud, IT Services, Digital Transformation, SaaS, Infrastructure, Data, or Security.
• Demonstrated experience acquiring new enterprise or mid-market customers.
• Experience carrying and achieving revenue or bookings targets.
• Experience managing complex B2B sales cycles involving multiple stakeholders.
• Strong understanding of cloud computing and enterprise technology.
• Working knowledge of Microsoft Azure and the Microsoft technology ecosystem.
• Ability to engage credibly with CIO/CTO/VP/Director-level stakeholders.
• Strong discovery, qualification, negotiation, and closing skills.
• Experience coordinating Presales and technical teams during sales cycles.
• Strong commercial acumen.
• Excellent communication, presentation, and executive-engagement skills.
• Strong CRM and forecasting discipline.
• Entrepreneurial mindset and bias for execution.

Desired Qualifications & Skills

• Previous experience selling Azure consulting, migration, modernization, or managed services.
• Experience working for a Microsoft Partner, cloud consulting company, MSP, or systems integrator.
• Existing relationships within the Microsoft field organization are highly desirable.
• Experience with Microsoft co-sell and partner-led GTM.
• Exposure to Azure Marketplace and Microsoft partner programs.
• Experience selling Data, AI, Generative AI, Security, DevOps, or Managed Services.
• Understanding of cloud economics and FinOps.
• Microsoft Azure Fundamentals or other Microsoft certifications are advantageous.
• MBA or equivalent business qualification is advantageous but not mandatory.

What We're Looking For

We are looking for someone who:

• Is fundamentally a hunter and new-business builder.
• Can open doors rather than depending entirely on inbound leads.
• Understands consultative technology selling.
• Is technically curious without trying to become the Solution Architect.
• Can engage both technology and business stakeholders.
• Builds strong relationships with Microsoft sellers and converts those relationships into opportunities.
• Knows when to qualify aggressively and when to walk away.
• Maintains strong pipeline and CRM discipline.
• Is comfortable navigating ambiguity while we rapidly scale our Azure business.
• Takes ownership from the first conversation through signed contract.
• Thinks beyond the initial transaction toward long-term customer value.

Why Join Comprinno

• Play a foundational role in building and scaling Comprinno's Microsoft Azure business.
• Sell transformation rather than a narrow technology product.
• Work across Cloud, Modernization, Security, Data, AI, Managed Services, and Platform Engineering.
• Build relationships with Microsoft and enterprise technology leaders.
• Work closely with experienced Cloud, AI, Presales, and Engineering teams.
• Participate directly in building a new growth engine within an established cloud technology company.
• Grow with an organization expanding from a strong AWS foundation into a broader multi-cloud and AI transformation company.

About Us

We believe the future of software development is AI-native — where engineers operate at a higher level of abstraction and quality remains non-negotiable. 

Incubyte is a software craft consultancy where the “how” of building software matters as much as the “what”.  

We partner with companies of all sizes, from helping enterprises build, scale, and modernize to early-stage founders bring their ideas to life. 

Our engineers operate in an AI-native development model, using AI as a collaborator across the SDLC to accelerate development while upholding the discipline of software craftsmanship. Guided by Software Craftsmanship and Extreme Programming practices, we build reliable, maintainable, and scalable systems with speed, without compromising quality. If this way of building software resonates with you, we’d like to talk. 

Our Guiding Principles 

These principles define how we work at Incubyte. They are non-negotiable. 

Relentless Pursuit of Quality with Pragmatism 

  We build high-quality systems without losing sight of delivery. 

Extreme Ownership 

  We take responsibility end-to-end for decisions, execution, and outcomes. 

Proactive Collaboration 

  We collaborate closely, challenge each other, and solve problems together. 

Active Pursuit of Mastery 

  We continuously improve our craft and raise our bar. 

Invite, Give, and Act on Feedback 

We seek, give, and act on feedback to get better every day. 

Ensuring Client Success 

We act as trusted partners and focus on real outcomes, not just output. 

Job Description

This is a remote position.

Experience Level

2+ years of experience in SQL, Python, and Snowflake (or equivalent cloud data warehouse), Azure Cloud services.

Role Overview

If you're a Data Craftsperson who takes pride in clean, well-tested data solutions and believes in the principles of Extreme Programming, we'd love to meet you. At Incubyte, we're a DevOps organization where developers own the entire release cycle — you'll get hands-on experience across data engineering, analytics, cloud infrastructure, and direct client communication. This role sits primarily in data engineering (80%) with a meaningful analytics component (20%), supporting our client's data systems end-to-end.

What You'll Do

• Design, build, and maintain data pipelines and infrastructure using SQL and Python
• Work within Snowflake to build and optimize data models supporting business use cases
• Parse and process structured and semi-structured data (JSON, XML) from varied sources
• Diagnose issues across raw, intermediate, and summary tables
• Build SQL queries to support repeatable analytics use cases based on stakeholder requirements
• Investigate and resolve data quality issues, including time-sensitive or urgent ones
• Identify opportunities to consolidate models and maintain a single source of truth (SSOT)

Requirements

What We're Looking For

• 2+ years of experience with SQL and relational databases, with the ability to understand complex data relationships and transformations (required)
• 2+ years of experience with Python for data engineering tasks (required)
• Experience with Snowflake or an equivalent cloud data warehouse (required)
• Experience working with Snowflake Coco or any other AI tools(required) 
• Experience parsing JSON and XML data (a plus)
• A strong eye for data quality and attention to detail
• Knowledge of Git (required)
• Knowledge of Azure cloud services such as Azure Data Factory, Azure Blob Storage, and Azure SQL Database (required)
• Knowledge of data infrastructure/modeling tools like DBT, Fivetran (a plus)
• Experience with BI tools like Power BI(a plus, not core to this role)
• Knowledge of Docker, Linux, Shell/Bash, and virtualization technologies (a plus)
• Knowledge of SSIS packages (a plus)
• Familiarity with CI/CD methodologies

Benefits

Life at Incubyte  

We are a remote-first company with structured flexibility. Teams commit to shared rhythms during core hours, ensuring smooth collaboration while maintaining autonomy. Twice a year, we come together in person for a co-working sprint and once a year for a retreat - with all travel expenses covered. 

Our environment is built for crafters: pairing, refactoring, experimenting with AI, and pushing the boundaries of software excellence. We are all lifelong learners, and our work is our passion. 

Perks

• Dedicated learning & development budget. 
• Sponsorship for conference talks. 
• Comprehensive medical & term insurance.
• Employee-friendly leave policies. 
• Home Office fund 
• Medical Insurance

Job Description

Position Title: Assistant Digital Marketing Manager

Role Type: Full-time | Individual Contributor

Department: Marketing

Location

Ahmedabad, Gujarat|On-site

Job Overview

We are looking for a highly driven, analytical, and hands-on Assistant Digital Marketing Manager to lead and execute our multi-channel digital growth strategies. Operating as a core individual contributor, you will take full end-to-end ownership of lead generation, marketing campaigns, search engine & generative engine optimization (SEO/GEO), social media management and outreach initiatives.

If you are a self-starter who thrives in a data-driven environment and excels at driving marketing-qualified leads (MQLs) through impactful digital execution, this role is for you.

Key Responsibilities

1. Lead Generation & MQL Pipeline

• Plan, execute, and optimize end-to-end multi-channel marketing campaigns focused on driving high-quality Marketing Qualified Leads (MQLs).
• Build and maintain lead conversion funnels, working continuously to improve lead quality, cost-per-acquisition (CPA), and pipeline conversion rates.
• Collaborate closely with sales/business development teams to align lead criteria, qualify MQLs, and refine lead-nurturing workflows.

2. Campaigns & Performance Marketing

• Directly design, set up, manage, and scale acquisition campaigns across channels (e.g., Google Ads, LinkedIn Ads, Meta Ads, etc.).
• Actively monitor daily campaign performance, budget utilization, and bid strategies to maximize return on ad spend (ROAS).
• Perform continuous A/B testing on ad copies, creatives, audiences, and landing page messaging to optimize click-through and conversion rates.

3. SEO & Generative Engine Optimization (GEO)

• Manage on-page, off-page, and technical SEO strategies to drive sustainable organic traffic growth.
• Implement cutting-edge GEO (Generative Engine Optimization) tactics to ensure brand visibility within AI-driven search environments (e.g., ChatGPT, Perplexity, Google Search Generative Experience/AI Overviews).
• Perform key phrase research, content optimization, and link-building strategies.

4. Social Media Marketing & Outreach

• Own and manage organic social media channels, developing engaging visual and written content that reinforces brand authority.
• Execute targeted outbound outreach strategies (e.g., cold outreach campaigns, partnership co-marketing, and influencer/industry network connections) to broaden brand reach.
• Track engagement, reach, and conversion metrics across all social profiles.

5. Data-Driven Analytics & Reporting

• Utilize marketing analytics platforms (e.g., Google Analytics 4, CRM platforms, platform-native ads managers) to track and analyze campaign performance in real time.
• Build actionable weekly and monthly performance dashboards highlighting key metrics (MQL volume, CPA, CTR, ROAS, pipeline impact).
• Leverage data and performance insights to continually iterate and pivot strategies for maximum impact.

Key Requirements & Qualifications

• Experience: 3–5 years of hands-on experience in performance marketing, digital lead generation, and digital channel management.
• Hands-on Execution: Proven track record as a self-sufficient individual contributor capable of executing campaigns from scratch without agency reliance.
• Core Skills:
• Demonstrated capability in driving MQLs through paid and organic channels.
• Hands-on experience managing PPC/Paid Social campaign setups and budgeting.
• Solid understanding of traditional SEO combined with awareness/experience in GEO strategies.
• End-to-end webinar management and digital outreach experience.
• Advanced proficiency with Google Analytics (GA4), marketing automation/CRM systems (e.g., HubSpot, Salesforce), and ad platforms.
• Mindset: Highly analytical, data-oriented, curious, and comfortable working independently in a fast-paced environment.

Solution Architect – AZURE Data Engineering

Job Overview

We are looking for an experienced Solution Architect – Data Engineering with strong expertise in designing data solutions and hands-on experience with Azure, Synapse, PySpark, Data Warehousing, and Data Lakes. The ideal candidate should have strong architectural and data engineering knowledge.

Key Responsibilities

• Design and implement scalable data architecture and solutions.
• Develop and manage Data Warehouse and Data Lake architectures.
• Design data platforms using Medallion Architecture.
• Lead Data Engineering and ETL activities.
• Work with Azure Synapse Analytics for data processing and analytics.
• Develop data solutions using PySpark / Apache Spark.
• Define and implement data validation and data quality processes.
• Collaborate with business, data, and technology teams to deliver effective data solutions.

Required Skills

• Strong experience in Solution Architecture / Data Architecture.
• Strong knowledge of Data Warehouse and Data Lake architecture.
• Good understanding of Medallion Architecture.
• Strong experience in Data Engineering and ETL.
• Hands-on experience with Microsoft Azure and Azure Synapse Analytics.
• Strong knowledge of PySpark / Apache Spark.
• Experience with Data Validation and Data Quality.
• Good communication and stakeholder management skills.
• 
  

Experience

8+ Years

About Us:

The QX Impact was launched with a mission to make A.I accessible and affordable and deliver AI Products/Solutions at scale for the enterprises by bringing the power of Data, AI, and Engineering to drive digital transformation. We believe without insights; businesses will continue to face challenges to better understand their customers and even lose them. Secondly, without insights businesses won't’ be able to deliver differentiated products/services; and finally, without insights, businesses can’t achieve a new level of “Operational Excellence” is crucial to remain competitive, meeting rising customer expectations, expanding markets, and digitalization.

Job Summary:

We are looking for a Senior Data Engineer who is creative, collaborative, and adaptable to join our agile team of data scientists, engineers, and UX developers. The role focuses on building and maintaining robust data pipelines to support advanced analytics, data science, and BI solutions.

As a Senior Data Engineer, you will work with internal and external data, collaborate with data scientists, and contribute to the design, development, and deployment of innovative solutions.

Key Responsibilities:

• Design, develop, test, and maintain optimal data pipeline and ETL architectures.
• Map out data systems and define/design required integrations, ETL, BI, and AI systems/processes.
• Prepare and optimize data for predictive and prescriptive modeling.
• Collaborate with teams to integrate ERP data into the enterprise data lake, ensuring seamless flow and quality.
• Enhance cloud data infrastructure on AWS or Azure for scalability and performance.
• Utilize big data tools and frameworks to optimize data acquisition and preparation.
• Build architectures to move data to/from data lakes and data warehouses for advanced analytics.
• Develop and curate data models for analytics, dashboards, and reports.
• Conduct code reviews, maintain production-level code, and implement testing approaches.
• Monitor, troubleshoot, and resolve data ingestion workflows to maintain reliability and uptime.
• Drive innovation and implement efficient new approaches to data engineering tasks.

Must-Have Skills:

• Bachelor’s degree in Computer Science, Mathematics, Engineering, or a related field.
• 5+ years of experience working with enterprise data platforms, including building and managing data lakes.
• 3–5 years of experience designing and implementing data warehouse solutions.
• Expertise in SQL, including developing stored procedures (SP) and applying advanced data design concepts.
• Proficiency in Spark (Python/Scala) and Spark Streaming for real-time data pipelines.
• Experience with AWS or Azure services (e.g., AWS Glue, Azure Data Factory, Redshift, Snowflake).
• Familiarity with big data tools such as Apache Kafka, Apache Spark, or Flink.
• Hands-on experience with orchestration tools (e.g., Apache Airflow, Prefect).
• Knowledge of CI/CD processes, version control (e.g., Git, Jenkins), and deployment automation.
• Strong problem-solving, communication, and collaboration skills.

Good-to-Have Skills:

• Experience in integrating ERP data into data lakes.
• Experience with traditional ETL tools (e.g., Talend, Pentaho).

Competencies:

• Tech Savvy - Anticipating and adopting innovations in business-building digital and technology applications.
• Self-Development - Actively seeking new ways to grow and be challenged using both formal and informal development channels.
• Action Oriented - Taking on new opportunities and tough challenges with a sense of urgency, high energy, and enthusiasm.
• Customer Focus - Building strong customer relationships and delivering customer-centric solutions.
• Optimize Work Processes - Knowing the most effective and efficient processes to get things done, with a focus on continuous improvement.

Why Join Us?

• Be part of a collaborative and agile team driving cutting-edge AI and data engineering solutions.
• Work on impactful projects that make a difference across industries.
• Opportunities for professional growth and continuous learning.
• Competitive salary and benefits package.

Application Details

Ready to make an impact? Apply today and become part of the QX Impact team!

Experience: 4+ Years 

Location: India (Bangalore, Hyderabad/ Mumbai/ Gurugram)

Role Summary:

AuxoAI is seeking a Senior GenAI Data Engineer with strong fundamentals in data engineering and end-to-end solution design. In this role, you will design and develop production-grade pipelines, leverage GenAI tools (Copilot, Claude, Gemini) to boost development productivity, and define engineering best practices across complex data environments. This is a highly collaborative, cross-functional role — ideal for someone who thrives at the intersection of data engineering excellence and GenAI-powered innovation.

Responsibilities:

• Architect and develop end-to-end data pipelines — from ingestion to transformation to consumption

• Lead solutioning and integration for complex data workflows (batch and streaming)

• Use AI-assisted coding tools (e.g., GitHub Copilot, Claude, Gemini) to accelerate code development, refactoring, and debugging

• Implement robust data quality, testing, lineage, and governance frameworks

• Drive best practices across pipeline performance, reusability, and scalability

• Mentor junior engineers and contribute to capability building within the data team

Requirement:

• 4+ years of experience in data engineering, with expertise in:

      o End-to-end pipeline development (batch and streaming)

      o Data modeling (dimensional, Data Vault, OBT)

      o ETL/ELT design patterns, performance tuning, and optimization

      o SQL (Advanced) and Python (Advanced)

      o Apache Spark for large-scale data processing

• Proficiency using AI coding tools (e.g., Copilot, Claude, Gemini) to enhance productivity and code quality

• Strong understanding of data quality frameworks, unit testing, and CI/CD for data workflows

• Experience with Google Cloud Platform services: o BigQuery, Dataflow, Cloud Composer, Pub/Sub, Dataproc, Vertex AI

• Exposure to finance or sales data domains

• Familiarity with Databricks, Delta Lake, or Apache Iceberg

• GCP Professional Data Engineer certification is a plus

About PortOne

PortOne is building the reconciliation and data intelligence layer for payments across Korea and international markets. We are a Series B startup backed by Softbank and Hanwa Capital, powering multi-billion dollars in annualised settlement volume for 2,000+ merchants across Korea, Thailand, Singapore, Indonesia, and beyond.

We are building AI-native products for leading brands — intelligent automation layers on top of complex financial data pipelines. If you want to work at the intersection of fintech, data engineering, and applied AI, this is your role.

Culture and Values

* You will be joining a team that stands for making a difference.

* You will be joining a culture that identifies more with Sports Teams rather than a 9 to 5 workplace.

* Your will have peers who are/have

** Highly Self Driven with A sense of purpose

** High Energy Levels - Building stuff is your sport

** Ownership - Solve customer problems end to end - Customer is your Boss

** Hunger to learn - Highly motivated to keep developing new tech skill sets

Your Work Ethic

* You are an athlete and building apps is your sport.

* Your passion drives you to learn and build stuff and not because your manager tells you to.

* You obsess over correctness — a bug in a settlement figure or a silent data drop is not acceptable to you.

* You have an eye for detail that most engineers skip past, and you take pride in getting it exactly right.

* Your work ethic is that of an athlete preparing for your next marathon. Your sport drives you and you like being in the zone.

* You are NOT a clockwatcher renting out your time, and NOT have an attitude of "I will do only what is asked for"

What will you do?

• Build and maintain financial data ingestion pipelines that pull settlement and transaction data from marketplace platforms (Amazon, Shopee, TikTok, Qoo10, Rakuten) on behalf of large brands operating across multiple Asian markets.
• Own reconciliation workflows end-to-end — from raw marketplace data to verified, merchant-ready settlement reports — ensuring every figure is correct and every discrepancy is surfaced, not swallowed.
• Design and implement AI-native features that automate financial analysis: agentic triage of settlement mismatches, root-cause detection across large transaction volumes, and intelligent alerting for ops and merchant teams.
• Instrument data quality and health monitoring so that silent failures — missing records, schema shifts, delayed ingestion — are caught before they reach the merchant.
• Build APIs and tooling that enable PortOne's ops and merchant success teams to investigate, verify, and close financial discrepancies faster and with more confidence.
• Expand platform coverage by integrating new marketplaces and new report types, working closely with data formats that are often inconsistent, undocumented, or changing without notice.
• Uphold rigorous engineering standards — correctness in financial data is not negotiable, and you treat edge cases and off-by-one errors with the same seriousness as a production incident.
• Uphold high engineering standards across codebases and processes.
• Collaborate with product, design, infrastructure, and operations stakeholders.

Skills and Experience

* Have ideally 2 to 4 Years of experience shipping high quality products/live features and workflows

* Strong backend engineering foundation — Go (Preferred), Python, or equivalent; REST/gRPC APIs; database design.

* Understands how to build scalable, resilient, and observable distributed systems.

* Must have built data flows and applications end to end taking full ownership

Preferred Skills and Background

*Prior experience/built apps in golang backend

*Data and data engineering background — comfortable with data pipelines, ETL/ELT patterns, event-driven architectures, reconciliation logic, or analytical workloads.

*AI-native development — you build products where AI is a first-class component, not a bolt-on.

*AI agentic development — experience building or working with agent frameworks, tool-use patterns, LLM orchestration, or automated reasoning pipelines.

Position Overview 

The AI Observability Engineer will be instrumental in implementation of scalable, cloud-native solutions to meet the growing needs of our Data & Development team. The successful candidate will demonstrate the ability to abstract complexity and create reusable, scalable patterns that accelerate development. The AI Observability Engineer will build and maintain a robust framework to ensure the reliability and maintainability of DPR Construction's complex AI systems. 

Responsibilities 

• Standardize observability practices across AI/ML and other development teams including logging, metrics, tracing, and model performance monitoring, ingesting data from multiple platforms 
• Lead hands-on implementation of automation-first DevOps and MLOps practices, enabling infrastructure-as-code and consistent, repeatable environment provisioning 
• Design and manage intelligent DataOps pipelines with automated data quality monitoring and anomaly detection 
• Deploy, maintain and monitor containerized ML workloads 
• Extend existing CI/CD pipelines to support automated infrastructure changes and ML workflows 
• Implement AI-driven data validation, schema and concept drift detection and metadata management. 
• Establish governance frameworks for AI systems, including bias detection, explainability, and auditability 
• Extend existing Azure RBAC strategy by automating role and permission management to reduce manual intervention 
• Develop automated test suites for model performance, regression, edge cases and bias validation 
• Monitor model KPIs (accuracy, precision, recall, latency, calibration) 
• Ensure reproducability of experiments and production models 
• Act as a technical point of contact for DevOps and MLOps practices, developing reusable patterns, documentation, and proof-of-concepts to drive adoption 

Qualifications 

• Bachelor’s degree in computer science, Data Science, Information Systems, or a related field 
• 5+ years of experience in DevOps, MLOps, Data Engineering, Software Engineering or Site Reliability Engineering 
• Strong understanding of cloud infrastructure and experience working with at least one major cloud provider, preferably Azure 
• Proficiency in at least one objected-oriented programming language, preferably python with hands-on experience in ml frameworks like TensorFlow, PyTorch or Scikit-learn 

Job Title : Senior Data Engineer – Databricks

Experience : 14 to 20 Years

Location : HSR Layout, Bangalore

Work Mode : Hybrid – 3 Days WFO

Shift : 11:30 AM – 07:30 PM IST

Positions : 2

Notice Period : Immediate Joiners Only

Interview : 1 Technical Round + 2 Client Rounds

Role Overview :

We are looking for a Senior Data Engineer to build and lead enterprise-scale data platforms for a Switzerland-based commodity client.

The role requires a strong hands-on Data Engineering professional with expertise in Databricks, PySpark, Python, SQL, and AWS, along with technical leadership and stakeholder management experience.

Must-Have Skills :

• 14 to 20 years of Data Engineering experience
• Databricks & Apache Spark / PySpark
• Python & SQL
• AWS Cloud
• Lakehouse Architecture
• ETL / ELT & Distributed Data Processing
• Batch & Streaming Pipelines
• Data Pipeline Optimization & Data Modeling
• CDC & Incremental Processing
• Git, CI/CD & Testing
• Data Quality, Monitoring & Observability
• Technical Leadership & Stakeholder Management

Key Responsibilities :

• Design and build scalable data pipelines using Databricks, PySpark, Python, SQL, and AWS.
• Own data products from design through production.
• Develop batch / streaming pipelines and reusable ETL / ELT frameworks.
• Optimize pipelines for performance, scalability, reliability, and cost.
• Design scalable data architectures and data models.
• Implement data quality, monitoring, lineage, and CI/CD practices.
• Lead technical discussions and mentor engineering teams.
• Collaborate with business stakeholders, architects, product owners, and engineering teams.
• Remain hands-on while providing technical leadership.

Ideal Candidate :

A 14 to 20 years experienced, hands-on Data Engineering leader with strong Databricks + PySpark + AWS expertise, excellent communication, stakeholder management, and experience delivering enterprise-scale data platforms.

🔴 Super Urgent : Only Bangalore-based immediate joiners.

Dear Candidate,

Greeting from NAM Info Pvt Ltd.

We have a role for Data Engineer position with NAM Info.

This role will be permanent with NAM info and deploy to client

location NEW DELHI~CHENNAI~HYDERABAD~PUNE~KOLKATA.

Work Mode: WORK FROM OFFICE

A decent hike can be provided based on current CTC

Interview Mode: Virtual

Role Descriptions:

Exp Range: 7 - 10 years

City Locations: NEW DELHI~CHENNAI~HYDERABAD~PUNE~KOLKATA

Key Responsibilities*

Role: Data Engineer

Location: ~NEW DELHI~CHENNAI~HYDERABAD~PUNE~KOLKATA

Skills: Digital: Databricks, Azure Data Factory

Experience Required: 8-10

Descriptions:

Good information and sound knowledge in Azure Synapse Analytics Azure Data Factory (ADF)Big Data technologies and data processing frameworks Azure Data Warehouse and associated Azure data platform services Data integration| data modelling| and performance optimization

Desire candidate

• Candidate should have valid PF.

Regards,

NAM Info 

Location – Hyderabad (Hybrid)

Work Experience – 5 to 7 years

CTC – upto 20 LPA

Roles & Responsibilities:

· We are looking for a Senior Data Engineering who will be majorly responsible for designing, building and maintaining ETL/ ELT pipelines.

· Integration of data from multiple sources or vendors to provide the holistic insights from data.

· You are expected to build and manage Data warehouse solutions, designing data models, creating ETL processes, implementing data quality mechanisms etc.

· Performs EDA (exploratory data analysis) required to troubleshoot data related issues and assist in the resolution of data issues.

· Should have experience in client interaction.

· Experience in mentoring juniors and providing required guidance.

Required Technical Skills

· Extensive hands on experience in Python, Pyspark, SQL, Dataiku.

· Strong experience in Data Warehouse, ETL, Data Modelling, building ETL Pipelines, Snowflake database.

· Working knowledge in Databricks, Redshift, ADF etc.

· Hands-on experience in cloud services like Azure, AWS- S3, Glue, Lambda, CloudWatch, Athena.

· Sound knowledge in end-to-end Data management, Data ops, quality and data governance.

· Familiar with SFDC, Waterfall/ Agile methodology.

· Strong domain knowledge in Pharma domain/ life sciences commercial data operations.

Qualifications

· Bachelor’s or master’s Engineering/ MCA or equivalent degree.

· 5-7 years of relevant industry experience as Data Engineer.

· Experience working on Pharma syndicated data such as IQVIA, Veeva, Symphony; Claims, CRM, Sales etc.

· High motivation, good work ethic, maturity, self-organized and personal initiative.

· Ability to work collaboratively and providing the support to the team.

· Excellent written and verbal communication skills.

· Strong analytical and problem-solving skills. 

Job Description:

Position: Lead / Senior Data Engineer

Location: Chennai

Shift: US Eastern Time ( 5:00 PM – 2:00 AM )

Experience : 8+ years

Notice Period: Immediate Joiner only

Roles and Responsibilities:

Role Overview

The Lead Data Engineer will be responsible for designing, developing, and delivering high-quality software and data solutions while leading a team of engineers. The role involves hands-on technical work, architectural decision-making, mentoring junior developers, and collaborating with cross-functional teams to ensure successful delivery of scalable data platforms and analytical solutions.

Key Responsibilities:

Lead the end-to-end design, development, and delivery of software systems, data pipelines, and components.

Define technical strategy, architecture, and best practices for development and data engineering.

Design and build optimized data pipelines using cutting-edge technologies in a cloud environment.

Construct infrastructure for efficient ETL processes from various sources and storage systems.

Architect, design, and maintain database pipeline architectures, ensuring readiness for AI/ML transformations.

Lead the implementation of algorithms and prototypes to transform raw data into useful information.

Review code for quality, scalability, and performance.

Collaborate with Product Managers, Business Managers, Designers, and QA teams to translate business requirements into technical solutions.

Develop analytical tools, programs, and reporting mechanisms.

Create data validation methods and data analysis tools.

Interpret data trends and patterns to establish operational alerts.

Conduct complex data analysis and present results effectively.

Prepare data for prescriptive and predictive modeling.

Ensure compliance with data governance and security policies.

Troubleshoot, debug, and resolve complex technical issues.

Drive continuous improvement in software and data development processes, tools, and methodologies.

Mentor and guide engineers through code reviews, technical discussions, and training.

Ensure timely delivery of projects while maintaining high engineering standards.

Continuously explore opportunities to enhance data quality and reliability.

Apply strong programming and problem-solving skills to develop scalable solutions.

Demonstrate passion for testing strategy, problem-solving, and continuous learning.

Willingness to acquire new skills and knowledge.

Possess a product/engineering mindset to drive impactful data solutions.

Experience working in distributed environments with global teams.

Stay current with emerging technologies and industry trends to propose innovative solutions.

Technical Skills and Experience Requirements

Minimum 8+ years of hands-on experience designing, building, deploying, testing, maintaining, monitoring, and owning scalable, resilient, and distributed data pipelines.

High proficiency in Python, Scala, and Spark for applied large-scale data processing.

Expertise with big data technologies, including Spark, Data Lake, Delta Lake, and Hive.

Solid understanding of batch and streaming data processing techniques.

Proficient knowledge of the Data Lifecycle Management process, including data collection, access, use, storage, transfer, and deletion.

Expert-level ability to write complex, optimized SQL queries across extensive data volumes.

Experience with RDBMS and OLAP databases such as MySQL and Snowflake.

Familiarity with Agile methodologies.

Obsession for service observability, instrumentation, monitoring, and alerting.

Knowledge or experience in architectural best practices for building data lakes.

Qualifications - Bachelor’s degree in computer science, Engineering, Information Systems, or related field.

About the Role

We are looking for a Senior Data Engineer with strong hands-on expertise in Databricks, Python, PySpark, and SQL to build scalable, high-performance data engineering solutions. You’ll architect and develop large scale, high-performance data pipelines capable of handling massive real-time and batch data volumes across multiple business systems. Databricks is the core enterprise data and processing platform for this role. You will also use Apache Airflow for workflow orchestration and dbt for ELT transformations, and will contribute to designing reliable, secure, and governed data platforms that enable analytics, reporting, and AI-driven use cases.

Key Responsibilities

• Design and implement large-scale data pipelines using Python/PySpark, Databricks, and Microsoft Fabric.
• Develop and optimize data processing workloads in Databricks using PySpark and Spark SQL, with a strong focus on scalability, reliability, performance, and maintainability.
• Develop and maintain dbt models including layered architecture, incremental models, snapshots, macros, testing, and documentation.
• Design, develop, and maintain Apache Airflow DAGs for orchestrating reliable, scalable, and observable data pipelines.
• Design and implement data quality, observability, and governance frameworks, including automated testing, monitoring, lineage, access control, and data privacy standards.
• Partner with analytics, product, and business stakeholders to turn requirements into trustworthy datasets, and raise the engineering bar through design discussions, code reviews, and mentoring junior engineers.

Required Skills

• Strong expertise in Python for developing scalable, modular, and production-ready data engineering applications.
• Strong expertise in PySpark, including DataFrame API, Spark SQL, Structured Streaming, partitioning strategies, joins, caching, handling data skew, and Spark performance optimization.
• Strong hands-on experience with Databricks for data ingestion, transformation, processing, and optimization, including Delta Lake, Unity Catalog, Databricks Workflows, notebooks, jobs, and Databricks-native data engineering capabilities.
• Strong experience in Databricks/Spark performance tuning, including query and job optimization, partitioning, file sizing, caching, join optimization, handling data skew, and efficient use of compute resources.
• Hands-on experience with Delta Lake, including transactional data processing, schema management, incremental data processing, and reliable batch and streaming data pipelines.
• Hands-on experience in developing dbt projects using layered architecture, incremental models, snapshots, macros/Jinja, testing, documentation, and deployment best practices.
• Expertise in advanced SQL and data modelling — dimensional modeling, slowly changing dimensions, schema evolution, and query optimization.
• Hands-on experience in developing and managing Apache Airflow DAGs, scheduling workflows, dependency management, retries, backfills, and operational monitoring.
• Hands-on experience with at least one major cloud platform (AWS, Azure or GCP).
• Strong problem-solving skills and the ability to work independently with business and analytics stakeholders.

Nice to Have

• Hands-on exposure to Microsoft Fabric for data integration and analytics.
• Experience using AI coding assistants (e.g. Claude Code, GitHub Copilot) as part of a development workflow.
• Familiarity with modern DevOps practices, including CI/CD pipelines, Infrastructure as Code (IaC), and containerization (Docker/Kubernetes).
• Domain expertise in financial services.

Job Summary

We are looking for a skilled and experienced Data Engineer to join our growing data team. The ideal candidate will have strong expertise in Python, PySpark, Data Modeling, and Power BI, with hands-on experience in designing, developing, and optimizing scalable data solutions. The role requires working closely with business stakeholders, data architects, and analytics teams to build robust data pipelines and semantic models that enable data-driven decision-making.

Technical Skills

• Strong hands-on experience in Python and PySpark development.
• Expertise in building and optimizing Data Engineering solutions and ETL pipelines.
• Strong understanding of Data Modeling concepts (Star Schema, Snowflake Schema, Dimensional Modeling).
• Experience with Power BI Data Modeling and Semantic Layer development.
• Proficiency in DAX (Data Analysis Expressions).
• Experience designing and managing Semantic Models in Power BI.
• Strong SQL skills and experience working with large datasets.
• Knowledge of data warehousing concepts and best practices.

Preferred Skills

• Experience with cloud platforms such as Azure, AWS, or GCP.
• Exposure to modern data platforms like Databricks.
• Understanding of data governance and data quality frameworks.

Job Description:

Position: Senior Data Engineer

Location: Chennai / Pune / Bangalore / Hyderabad

Working Type: WFO

Shift: UK Shift (2:00 – 11:00 PM)

Experience : 7+ years overall

Interviews: Assessment || 2 Interview rounds.

Notice Period: Immediate Joiner

Key Responsibilities

Implement ingestion, transformation, and optimization of enterprise data sources into Microsoft Fabric Lakehouse environments.

Configure and optimize Fivetran connectors (Oracle, SQL DB, etc.)

Manage large-volume ingestion and backfill operations

Implement Bronze to Silver transformation pipelines

Develop incremental load and CDC logic

Optimize Lakehouse performance and storage patterns

Implement monitoring (record counts, load duration, failure tracking)

Support Dev/Test/Prod promotion processes

Required Qualifications

7+ years of data engineering experience

Hands-on experience with Microsoft Fabric or Azure Synapse/Data Factory

Strong experience with Fivetran or similar ELT tools

Experience handling high-volume datasets (hundreds of millions of records)

Proficiency in SQL, Python, and data modeling concepts

Strong understanding of Medallion architecture.

Roles & Responsibilities

• Design, develop, and deliver scalable end-to-end data pipelines using Azure Data Factory, ensuring robust integration

of enterprise-wide data from diverse sources

• Build and optimize data engineering workflows using Databricks and PySpark

• Write efficient, high-performance SQL for data transformation and analysis

• Work with the Azure Cloud platform and associated services, applying strong understanding of data warehousing,

data models, and pipelines

• Provide technical leadership to a team of developers, including code reviews and enforcing best practices across the

development lifecycle

• Oversee CI/CD implementation using Azure DevOps, managing deployments across development, QA, and production

environments with proper change control processes

• Collaborate with cross-functional teams to translate business requirements into scalable data solutions

• Ensure data quality, reliability, and performance across all pipelines and platforms

Ideal Candidate

1Strong Azure Databricks Engineer / Senior Data Engineer Profile

2Mandatory (Experience 1) – Must have minimum 8+ years of overall experience in Data Engineering, Data Development, or related data technology roles, with strong hands-on experience in enterprise data pipeline development.

3Mandatory (Experience 2) – Must have strong hands-on experience with Azure Databricks, including development and optimization of scalable data engineering workflows using Databricks and PySpark.

4Mandatory (Experience 3) – Must have strong hands-on proficiency in PySpark/Python and SQL, with proven experience developing complex data transformations, processing workflows, and performance-optimized queries.

5Mandatory (Experience 4) – Must have hands-on experience with Azure Data Factory (ADF) for designing, developing, and orchestrating end-to-end data pipelines and integrating data from multiple sources.

6Mandatory (Experience 5) – Must have strong experience working on the Azure Cloud platform and associated data services, with solid understanding of data warehousing, data modeling, pipeline architecture, and enterprise data solutions.

7Mandatory (Experience 6) – Must have hands-on experience implementing CI/CD using Azure DevOps, including deployment and release management across development, QA, and production environments.

8Mandatory (Experience 7) – Must have proven technical leadership experience, including code reviews, enforcing development best practices, mentoring developers, and providing technical guidance to a data engineering team.

9Mandatory (Notice Period) – Immediate joiners or candidates who can join within 15 days.

10Mandatory (Note) - The position is open across all Cognizant offices pan India. Candidates must be willing to attend the F2F interview at the nearest Cognizant office location.

Experience: 4–6 Years

Domain: Automotive IoT | Connected Vehicles | Firmware & OTA

Core Stack: Rust | AWS | IoT Core | Apache Kafka | MemoryDB

Key Responsibilities  

OTA & Firmware Lifecycle  

• Co-own OTA firmware rollout operations across multi-ECU connected vehicle architectures.
• Design automated mechanisms to detect update failures, network interruptions, verification errors, and stalled deployments.
• Implement deterministic retry, recovery, and rollback mechanisms to ensure reliable firmware updates without vehicle bricking.
• Ensure firmware package integrity, signature validation, and data security throughout the OTA pipeline.

Data Engineering & Telemetry  

• Design and maintain real-time streaming pipelines for vehicle telemetry, heartbeats, OTA campaign status, and ECU state changes.
• Build high-throughput data services and workers using Rust for payload routing, processing, and verification.
• Use Apache Kafka and AWS IoT Core for real-time data ingestion and event streaming.
• Leverage AWS MemoryDB for Redis for low-latency fleet state, campaign progression, and session management.
• Build resilient pipelines capable of handling intermittent connectivity, noisy networks, and out-of-order data.

Monitoring & Analytics  

• Build real-time dashboards for fleet health, firmware versions, OTA campaigns, and update progress.
• Define and monitor key OTA metrics including success/failure rates, retry rates, failure categories, and completion time.
• Implement automated alerting and anomaly detection for unexpected failure spikes during staged or canary rollouts.
• Analyze logs, traces, and telemetry data to identify campaign bottlenecks, telemetry loss, and hardware-related failures.

Required Skills  

Mandatory  

• 4–6 years of experience in Data Engineering, Software Engineering, or IoT Backend Engineering.
• Strong hands-on experience with Rust for backend/data processing applications.
• Experience with AWS, particularly IoT Core, S3, ECS/EKS, and Lambda.
• Strong experience with Apache Kafka and real-time data pipelines.
• Hands-on experience with AWS MemoryDB for Redis or Redis Enterprise.
• Working knowledge of Python and SQL.
• Experience with MQTT, WebSockets, and HTTP/S protocols.
• Strong understanding of distributed systems, streaming data, and resilient data pipelines.

Preferred  

• Experience with firmware lifecycle management and OTA systems.
• Exposure to connected vehicles, automotive IoT, telemetry platforms, or connected hardware fleets.
• Experience with device shadows and fleet/device state management.
• Experience building telemetry dashboards using Power BI, Apache Superset, or custom dashboards.
• Experience with staged/canary deployments and automated failure recovery.

Primary Technology Stack  

• Languages: Rust, Python, SQL
• Cloud: AWS, IoT Core, S3, ECS/EKS, Lambda
• Streaming: Apache Kafka
• Caching & State: AWS MemoryDB for Redis, Redis
• IoT Protocols: MQTT, WebSockets, HTTP/S
• Analytics & Visualization: Power BI, Apache Superset
• Domain: Automotive IoT, Vehicle Telemetry, OTA, Firmware Management

Strong Databricks Architect Profile with end-to-end Lakehouse ownership

2

Mandatory (Experience 1) – Must have 10+ years of software engineering experience with atleast 5+ years in Data Engineering with hands on exposure to Databricks and strong ownership of end-to-end data pipeline development.

3

Mandatory (Experience 2) – Must have atleast 5+ years of expertise across the Databricks ecosystem — Delta Lake, Delta Live Tables, Autoloader, Structured Streaming, Workflows, Unity Catalog

4

Mandatory (Tech skill 1) – Must have worked at architecture level, owning end-to-end design through deployment

5

Mandatory (Tech skill 2) – Must have strong experience with Python and SQL for data processing and Apache Spark for performance tuning & scalability

6

Mandatory (Tech skill 3) – Must have experience in large-scale data warehousing & advanced data modeling (3NF and dimensional) across batch and real-time systems

7

Mandatory (AI Exposure) – Must have at least a basic working understanding of how AI services or tools work

8

Mandatory (Communication) – Must have strong stakeholder management & requirement-gathering experience with US or UK clients

9

Mandatory (Company) – Must come from a B2B IT services or IT consulting background

10

Mandatory (Note) – CTC is inclusive of 5% variable

11

Preferred (Tech skill 1) – Azure Databricks or Azure data services experience (project runs on Azure DevOps)

12

Preferred (Tech skill 2) – MLflow or MLOps practices and AI use cases (RAG, AI/BI)

13

Preferred (Tech skill 3) – CI/CD, Databricks Asset Bundles (DABs) or equivalent packaging, Terraform or IaC, reusable deployment templates

14

Preferred (Integrations) – ServiceNow or enterprise system integrations

15

Preferred (Certifications) – Databricks (Data Engineer Associate or Professional, ML or GenAI tracks), Azure or AWS cloud certifications

Skills Referential (Required knowledge, skills and abilities)

Technical Skills:

Python

Pyspark

SQL

ETL Aws, Azure, gcp

Key Responsibilities  

• Design, build, and optimize scalable data pipelines for AI/ML applications.
• Develop, train, evaluate, and deploy Machine Learning and Deep Learning models.
• Build production-ready LLM applications using Retrieval-Augmented Generation (RAG), prompt engineering, and vector databases.
• Fine-tune open-source and foundation models using domain-specific datasets.
• Develop and maintain end-to-end MLOps pipelines for model deployment, monitoring, and lifecycle management.
• Perform data preprocessing, feature engineering, exploratory data analysis (EDA), and model evaluation.
• Develop APIs and AI services for production deployment.
• Collaborate with cross-functional teams to deliver scalable AI-driven solutions.
• Monitor model performance, troubleshoot production issues, and maintain technical documentation.

Required Skills  

Mandatory  

• 1–3 years of experience in Data Science, Data Engineering, or AI/ML development.
• Strong programming skills in Python and SQL.
• Hands-on experience with Machine Learning frameworks such as PyTorch, TensorFlow, or Scikit-learn.
• Experience building LLM-powered applications using RAG, Prompt Engineering, and Embeddings.
• Hands-on experience with LangChain, LlamaIndex, CrewAI, or n8n for LLM orchestration and AI workflow automation.
• Experience in LLM fine-tuning and working with Hugging Face models.
• Knowledge of MLOps concepts including model deployment, monitoring, versioning, and CI/CD.
• Experience with Git, REST APIs, Linux environments, and data processing libraries.

Preferred  

• Experience with vector databases such as Pinecone, Chroma, Milvus, or Weaviate.
• Familiarity with Docker, Kubernetes, and MLflow.
• Exposure to Apache Spark or Airflow for data engineering workflows.
• Experience with cloud platforms (AWS, Azure, or GCP).

Primary Technology Stack  

• Languages & Data Processing: Python, SQL, Pandas, NumPy, Apache Spark
• AI & Machine Learning: PyTorch, TensorFlow, Scikit-learn
• Application Frameworks: LangChain, LlamaIndex, CrewAI, n8n
• Core Methodologies: Retrieval-Augmented Generation (RAG), Model Fine-Tuning, Prompt Engineering, Embeddings
• Models & Infrastructure: OpenAI APIs, Hugging Face Ecosystem, Embedding Models
• Vector Databases: Pinecone, Chroma, Milvus, Weaviate
• Databases: PostgreSQL, MongoDB
• MLOps & DevOps: Docker, Kubernetes, MLflow, CI/CD, Git
• Cloud Platforms: AWS, Azure, GCP

Experience: 1–3 Years

Domain: Data Science | Data Engineering | Machine Learning | Generative AI | MLOps

About AuxoAI:

AuxoAI is a global platform-based services firm. We help companies—turn their strategies into practical digital and AI solutions. By understanding how our clients make decisions, we use digital and Artificial Intelligence (AI) technologies to drive growth, enhance their operations, improve customer experiences, and provide clear, actionable insights from their data. What We Do We work across various industries such as healthcare, high-tech, consumer packaged goods (CPG), finance etc., and in sales, marketing, and customer support functions.

We help our clients with accelerating their digital and AI journeys through:

• AI Application Development

• Data, Digital and Cloud acceleration using AI

• AI Native Product Engineering

We are seeking a skilled and experienced Data Engineer to join our dynamic team. The ideal candidate will have 6+ years of prior experience in data engineering, with a strong background in AWS (Amazon Web Services) technologies. This role offers an exciting opportunity to work on diverse projects, collaborating with cross-functional teams to design, build, and optimize data pipelines and infrastructure.

Responsibilities:

* Design, develop, and maintain scalable data pipelines and ETL processes leveraging AWS services such as S3, Glue, EMR, Lambda, and Redshift.

* Collaborate with data scientists and analysts to understand data requirements and implement solutions that support analytics and machine learning initiatives.

* Optimize data storage and retrieval mechanisms to ensure performance, reliability, and cost-effectiveness.

* Implement data governance and security best practices to ensure compliance and data integrity.

* Troubleshoot and debug data pipeline issues, providing timely resolution and proactive monitoring.

* Stay abreast of emerging technologies and industry trends, recommending innovative solutions to enhance data engineering capabilities.

Requirements :

* Bachelor's or Master's degree in Computer Science, Engineering, or a related field.

* 6+ years of prior experience in data engineering, with a focus on designing and building data pipelines.

* Proficiency in AWS services, particularly S3, Glue, EMR, Lambda, and Redshift.

* Strong programming skills in languages such as Python, Java, or Scala.

* Experience with SQL and NoSQL databases, data warehousing concepts, and big data technologies.

* Familiarity with containerization technologies (e.g., Docker, Kubernetes) and orchestration tools (e.g., Apache Airflow) is a plus.

Description:

Analytical Engineer with strong Data Analyst and Data Modelling expertise required to translate business requirements into structured, analytics-ready datasets. Must have experience in Data Vault 2.0 / dimensional modelling and advanced SQL for data transformation and analysis. Role focuses on data profiling, validation, and delivery of trusted data for reporting and analytics. Experience with Azure/Databricks and enterprise data environments preferred. Strong stakeholder engagement and ability to bridge business and technical data requirements essential.

Must Have Skills

• Data Analysis
• Data Governance
• Data Modeling tool

Nice to Have Skills

• Business writing skills
• Governance, Risk and Controls
• Principles of project management
• Relevant regulatory knowledge
• Relevant software and systems knowledge

Job Title: Data Engineer – PySpark | Oracle | GCP

Experience: 5–7 Years

Location: Hyderabad

Notice Period: Immediate Joiners Preferred

Job Summary

We are seeking an experienced Data Engineer with strong expertise in PySpark, Oracle, and Google Cloud Platform (GCP) to design, develop, and optimize scalable data pipelines. The ideal candidate should have hands-on experience in ETL development, data integration, and cloud-based data engineering solutions.

Key Responsibilities

• Design, develop, and maintain scalable ETL/data pipelines using PySpark.
• Extract, transform, and load data from Oracle databases into GCP environments.
• Build and optimize batch data processing workflows for high performance and reliability.
• Develop data engineering solutions using GCP services.
• Ensure data quality through validation, monitoring, and troubleshooting.
• Optimize SQL queries and ETL jobs for performance and scalability.

Required Skills

• 5–7 years of experience as a Data Engineer.
• Strong hands-on experience with PySpark.
• Solid experience with Oracle Database and advanced SQL.
• Hands-on experience with Google Cloud Platform (GCP).
• Strong understanding of ETL processes and data warehousing concepts.

Work Location: Hyderabad

Notice Period: Immediate Joiners Preferred

Title                                 : Snowflake Cortex AI Engineer

Experience                    : 5+ years

Location                         : Remote

Work type                      : Remote

Employment Type      : Full Time

Notice Period               : Immediate

Work Day                      : Mon to Fri

About the Role:

We are seeking a highly skilled Snowflake Cortex AI Engineer with 5+ years of experience in Data Engineering, AI, and Snowflake. The ideal candidate will have hands-on expertise in Snowflake Cortex AI, Snowpark, and Generative AI capabilities to build intelligent, scalable, and secure AI-powered data applications. The role involves designing AI-driven solutions, integrating LLM capabilities into enterprise workflows, and collaborating with cross-functional teams to deliver business value.

Key Responsibilities:

• Design, develop, and implement AI-powered solutions using Snowflake Cortex AI.
• Build intelligent data applications leveraging Snowpark, Cortex AI functions, and SQL.
• Develop Retrieval-Augmented Generation (RAG) solutions using enterprise data stored in Snowflake.
• Integrate Large Language Models (LLMs) into enterprise applications using Snowflake Cortex.
• Design and optimize AI workflows for document summarization, sentiment analysis, classification, translation, question answering, and text generation.
• Develop scalable data pipelines to support AI and machine learning workloads.
• Collaborate with Data Engineers, Data Scientists, and business stakeholders to understand AI use cases and deliver effective solutions.

Ensure AI solutions comply with enterprise security, governance, and data privacy standards.

• Optimize Snowflake performance and AI workloads for scalability and cost efficiency.
• Participate in architecture discussions, code reviews, and technical documentation.

Required Skills & Experience

• 5+ years of experience in Data Engineering, AI/ML, or Analytics.
• Strong hands-on experience with Snowflake.
• Experience working with Snowflake Cortex AI capabilities.
• Strong understanding of Snowpark (Python or SQL).
• Experience building AI-powered applications using enterprise data.
• Hands-on experience with Python and SQL.
• Knowledge of Generative AI, Prompt Engineering, and Large Language Models (LLMs).
• Experience designing and implementing RAG (Retrieval-Augmented Generation) solutions.
• Strong understanding of data modeling and data warehousing concepts.
• Experience developing and optimizing ETL/ELT pipelines.
• Experience integrating REST APIs and external AI services. 

Technical Skills

• Snowflake
• Snowflake Cortex AI
• Snowpark
• SnowSQL
• Snowpipe
• Streams & Tasks
• Secure Data Sharing
• Performance Optimization
• Role-Based Access Control (RBAC) 

Programming

• Python
• SQL
• AI & Machine Learning
• Generative AI
• Large Language Models (LLMs)
• Prompt Engineering
• Retrieval-Augmented Generation (RAG)
• AI Model Integration
• Text Analytics
• Semantic Search
• Data Engineering
• ETL/ELT Development
• Data Warehousing
• Data Pipelines
• Structured & Semi-Structured Data Processing
• Cloud (Preferred)
• AWS / Azure / GCP

Preferred Skills

• Experience with vector search and semantic search concepts.
• Knowledge of Snowflake Cortex Analyst, Cortex Search, or Cortex Agents.
• Experience with AI governance and responsible AI practices.
• Familiarity with LangChain, LangGraph, or similar AI orchestration frameworks.
• Exposure to ML model deployment and MLOps practices.
• SnowPro certification is an added advantage. 

Educational Qualification

• Bachelor's or Master's degree in Computer Science, Information Technology, Data Science, Artificial Intelligence, Engineering, or a related field.

Key Competencies

• Strong analytical and problem-solving skills.
• Excellent communication and stakeholder management abilities.
• Ability to translate business requirements into AI-driven solutions.
• Strong collaboration and teamwork skills.
• Self-driven with the ability to work independently in a remote environment.

Job description

We are seeking an experienced and highly skilled Workato Developer to lead integration efforts between Salesforce, enterprise data warehouses, and other source systems. The ideal candidate will have deep experience in ETL processes, data migration, and ongoing data synchronization using Workato. You will be responsible for building scalable, secure, and performant integrations for a large enterprise environment. Key Responsibilities:

• Design, develop, and maintain data integrations between Salesforce and external systems (e.g., data warehouse, ERP, marketing platforms) using Workato. • Implement ETL workflows for both one-time data migrations and ongoing bi-directional data sync. • Create, configure, and maintain recipes, connections, and custom connectors in Workato. • Collaborate with Salesforce developers, data engineers, and business analysts to understand integration needs and translate them into technical solutions. • Monitor and optimize performance of integration flows and ensure data accuracy, error handling, and logging. • Develop and maintain integration documentation, data mapping, and transformation logic. • Work closely with data warehouse teams to enable seamless data exchange and transformation. • Support data quality initiatives by identifying data anomalies and recommending corrective actions. • Ensure compliance with enterprise security and data governance standards. Required Qualifications:

• 5–7 years of professional experience in data integration, ETL, or data engineering roles. • Hands-on experience with Workato or similar iPaaS platforms (e.g., MuleSoft, Boomi, SnapLogic) with proven track record of successful Salesforce integrations. • Strong expertise in integrating Salesforce with other enterprise systems (ERP, marketing tools, databases, APIs, etc.). • Proficiency in data transformation, JSON/XML, REST/SOAP APIs, and SQL. • Experience working in large enterprise environments with complex data landscapes. • Solid understanding of Salesforce data model, objects, and API limits. • Familiarity with data warehousing concepts, such as dimensional modeling, data marts, and analytics pipelines. • Bachelor’s degree in Computer Science, Information Systems, or a related field. Preferred Qualifications:

• Workato Certification or similar iPaaS certifications. • Experience with CI/CD, version control (Git), and Agile methodologies. • Exposure to Salesforce Data Loader, Salesforce Connect, or external object configurations

At Mitratech, we are a team of technocrats focused on building world-class products that simplify operations in the Legal, Risk, Compliance, and HR functions. We are a close-knit, globally dispersed team that thrives in an ecosystem that supports individual excellence and takes pride in its diverse and inclusive work culture centered around great people practices, learning opportunities, and having fun! Our culture is the ideal blend of entrepreneurial spirit and enterprise investment, enabling the chance to move at a rapid pace with some of the most complex, leading-edge technologies available.

For over 35 years, the experts at Mitratech have been focused on solving the complex needs. Today, we serve 20,000 client companies of all sizes globally, representing 30% of the Fortune 500 and over 500,000 users in over 160 countries.

As we continue to grow, we’re always looking for resourceful, enthusiastic, and fresh perspectives. Join our global team and see what makes Mitratech a truly exceptional place to work!

Job Overview 

Principal Data Engineer

About Engineering at Mitratech Legal Solutions 

Mitratech's engineering organization is a collaborative and dynamic environment where engineers are empowered to drive technical direction and innovation. Our engineers are passionate about delivering high-quality products and solutions that meet the evolving needs of our customers, and we're committed to fostering a culture of continuous learning and growth. 

About the Role 

Mitratech is a fast-paced and dynamic environment, and this role requires someone who is adaptable, resilient, and able to thrive in a rapidly changing landscape. If you’re a seasoned engineer with a passion for technical leadership, innovation, and collaboration — including building the data foundations that power trusted reporting and agentic AI-driven products — we’d love to hear from you. 

What You Will Do

•  Drive technical direction for a significant product domain or platform capability, ensuring alignment with business objectives and customer needs 

•  Design and maintain data pipelines and reporting models that power trusted business metrics and increasingly feed agentic AI systems (e.g., RAG ingestion, embeddings, vector stores, AI agent workflows) 

•  Use AI-assisted and agentic engineering tools (e.g., Claude Code, Copilot, Cursor, AI agents) as part of your own workflow, and help other engineers adopt agentic development practices effectively 

•  Reduce systemic complexity by identifying and leading architectural debt remediation, and developing strategies for ongoing technical debt management 

•  Partner with Product and Engineering leadership to inform multi-quarter roadmap feasibility, and provide technical guidance and oversight to ensure successful implementation 

•  Elevate engineering craft across multiple teams through RFCs, mentorship, and knowledge sharing, and develop training programs to improve engineering skills and knowledge 

•  Represent Mitratech’s technical capabilities externally, including speaking at conferences, contributing to open-source projects, and engaging with industry peers and thought leaders 

What We Are Looking For 

To be successful in this role, you will need: 

•  10+ years of experience in software engineering, with a focus on technical leadership and architecture 

•  Deep understanding of data engineering principles, including data modeling, data warehousing, reporting, and data governance 

•  Strong technical expertise in SQL, PostgreSQL, ETL/ELT pipelines, BI tools, and analytics platforms 

•  Practical experience with AI/LLM-adjacent and agentic AI data work — e.g., RAG ingestion pipelines, embedding generation, vector store management, or building/operating AI agent workflows over data — using AI coding assistants (Claude Code, Copilot, Cursor, or similar) as a regular part of the engineering workflow 

•  Working knowledge of modern cloud platforms such as AWS 

•  Experience with BI, reporting, dashboards, and customer-facing analytics 

•  Experience leading cross-functional initiatives with product, engineering, analytics, and business teams 

Nice to Have 

•  Working knowledge of Ruby on Rails and React 

•  Experience with a semantic or metrics layer (e.g., dbt Semantic Layer, headless BI) 

•  Understanding of CI/CD, Git-based workflows, and infrastructure-as-code 

The Stack Context 

•  Modern data stack: Fivetran, Airbyte, dbt, Snowflake, GitHub, Terraform, or similar tools 

•  Application context (nice to have): Ruby on Rails, React, or similar backend/frontend frameworks 

•  Data modeling: SQL, analytics models, documentation, testing, naming standards, and version control 

•  Infrastructure: cloud-based data infrastructure, infrastructure-as-code, CI/CD, monitoring, and cloud storage 

•  Data workflows: ingestion, transformation, orchestration, reporting, deployment, and change management 

•  Reporting focus: trusted metrics, scalable reporting models, dashboards, exports, and data quality 

•  AI surface: data pipelines and quality practices supporting AI/LLM and agentic AI use cases (RAG, embeddings, vector stores, AI agents) alongside traditional BI 

Why This Role 

This role offers a unique opportunity to drive technical direction and innovation at a rapidly growing company, while also mentoring and coaching engineers to improve their craft. As a Principal Data Engineer at Mitratech, you will have the chance to work on complex and challenging problems spanning trusted reporting and agentic AI systems, collaborate with cross-functional teams, and represent the company's technical capabilities externally. If you're looking for a role that offers a mix of technical leadership, data and reporting depth, agentic AI innovation, and collaboration, this could be the perfect fit for you. 

We are an equal-opportunity employer that values diversity at all levels. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, national origin, age, sexual orientation, gender identity, disability, or veteran status.

Job Title: Sales Engineer

We are hiring a Sales Engineer to drive business growth by promoting and selling engineering products and technical solutions to industrial and commercial clients. The ideal candidate will combine technical knowledge with strong sales and relationship-building skills to understand customer requirements and recommend suitable solutions.

Key Responsibilities:

• Identify and develop new business opportunities through field visits and client meetings.
• Promote engineering products and provide technical product demonstrations.
• Understand customer requirements and prepare quotations and proposals.
• Negotiate contracts and close sales while ensuring customer satisfaction.
• Build and maintain long-term relationships with customers and channel partners.
• Coordinate with internal teams for order execution and after-sales support.
• Maintain accurate sales reports, customer records, and market feedback.
• Achieve assigned sales targets and contribute to business growth.

Requirements:

• Bachelor's degree in Mechanical, Electrical, Civil, Instrumentation Engineering, or a related field.
• 2–5 years of experience in technical or engineering sales (freshers with relevant technical knowledge may also apply).
• Strong communication, negotiation, and presentation skills.
• Ability to read technical drawings and specifications.
• Proficiency in MS Office and CRM software.
• Valid driving license and willingness to travel extensively for business development. 

15+ years of IT Delivery and Technology Services experience.

15+ years managing large offshore delivery organizations.

Proven leadership of portfolios exceeding 200+ resources.

Financial Services or Banking experience is a MUST

Extensive technical experience in Data Engineering, Data Platforms, Data Brics.

Cloud Data Transformation Programs, Data Warehousing, Azure Data Platform and compliance experience

Job Title : Data Platform Engineer (SDE 2 / SDE 3)

Experience : 3 to 8 Years (SDE2 : 3 to 5 Years | SDE3 : 5.5 to 8 Years)

Location : Remote (Contract) → Pune (Post Conversion)

Employment Type : Contract-to-Hire (3 Months)

Mandatory Skills :

Java / Python / Go, SQL, Data Engineering, ETL / ELT, Apache Airflow, Apache Spark / Flink, Kafka, AWS/GCP/Azure, Distributed Systems, API Development, Docker, Kubernetes, CI/CD, System Design

Role Overview :

We are looking for a Data Platform Engineer with strong backend engineering expertise to build scalable, high-performance data platforms and distributed systems. The ideal candidate should have hands-on experience in developing production-grade backend applications along with designing and maintaining modern data pipelines.

Key Responsibilities :

• Build and maintain scalable ETL/ELT and data processing pipelines.
• Develop backend services and APIs using Java (preferred), Python, or Go.
• Design batch and real-time data pipelines using Spark, Flink, Kafka, and Airflow.
• Optimize SQL queries, data models, and distributed data systems.
• Work with cloud platforms (AWS, GCP, or Azure) and container technologies (Docker, Kubernetes).
• Implement CI/CD, monitoring, logging, and performance optimization.
• Collaborate with product and engineering teams on scalable system design and architecture.

Required Qualifications :

• SDE 2 : 3 to 5 years of Backend + Data Engineering experience.
• SDE 3 : 5.5 to 8 years of Backend + Data Engineering experience.
• Strong coding skills in Java (preferred), Python, or Go.
• Excellent SQL and data modeling knowledge.
• Hands-on experience with Airflow, Apache Spark / Apache Flink, or similar technologies.
• Experience building scalable backend services and APIs.
• Good understanding of distributed systems, system design. and scalable architecture.
• Experience with cloud platforms (AWS, Azure, or GCP).
• Hands-on experience with Docker and Kubernetes.
• Strong understanding of CI/CD pipelines.
• Excellent problem-solving and debugging skills.

Good to Have :

• Experience with Snowflake, BigQuery, or Redshift.
• Understanding of Kafka, Kinesis, or Pub/Sub
• Performance optimization of large-scale distributed systems
• FinTech or high-scale distributed systems experience.
• Knowledge of data governance, security, and compliance.

Preferred Candidate Profile :

• Strong Backend + Data Engineering experience
• Experience building scalable production systems
• Strong ownership mindset
• Good system design knowledge
• Experience processing millions of events using Kafka/Spark
• Built APIs supporting data infrastructure
• Production engineering experience

Interview Process :

1. Take-home Coding Assignment (48 Hours)
2. Leadership & Strategy Round (1 Hour)
3. Technical Depth – Data Engineering & Performance (1 Hour)
4. Culture & Values Fit (30 Minutes)

Requirements:

• 5+ years in data architecture/data engineering, with at least 2+ years in an architect or lead capacity.
• Strong SQL: advanced query optimisation, indexing, partitioning strategies.
• Data modeling dimensional modelling (star/snowflake schema), normalization/denormalization tradeoffs, entity relationship design.
• Cloud data platforms: hands-on with AWS (Redshift, S3 Glue), Azure (Synapse, Data Factory), or GCP (BigQuery, Dataflow).
• Big data ecosystems: Spark, Hadoop, or Kafka for large-scale/streaming data.
• Data warehousing Snowflake, Redshift, BigQuery, or Databricks.
• ETL/ELT pipeline design: Airflow, dbt, Fivetran, or similar orchestration tools.
• Data governance & security: data lineage, access control, compliance (GDPR/SOC2), master data management.

Strongly Preferred:

• Experience architecting systems supporting ML/AI pipelines (feature stores, vector DBs, real-time inference data flows).
• Programming in Python or Scala for pipeline development.
• API/microservices architecture exposure, understanding how data systems integrate with application layers.
• Experience with data mesh/data lake house architectures.
• Prior experience presenting architecture decisions to leadership/stakeholders.

Nice-to-Have (Differentiators):

• Certifications: AWS/GCP/Azure data architecture certs.
• Experience in a high-growth startup (built systems from scratch, not just maintained legacy).
• Exposure to real-time/streaming architecture (Kafka, Kinesis, Flink).

We are looking for a skilled Python & PySpark Developer with strong expertise in Big Data technologies, Spark, SQL/PL-SQL, and REST API development using Flask or Django. The ideal candidate should have experience building scalable data pipelines, processing large datasets, developing APIs, and working with distributed computing frameworks.

Key Responsibilities

• Develop, optimize, and maintain scalable data pipelines using PySpark and Apache Spark.
• Design, develop, and optimize complex SQL and PL/SQL queries, stored procedures, functions, and database objects.
• Build and maintain RESTful APIs using Flask or Django.
• Develop robust Python applications for data engineering and backend services.
• Process and analyze large-scale datasets using Big Data technologies.
• Optimize Spark jobs for performance, scalability, and reliability.
• Integrate APIs with internal and external systems.
• Collaborate with cross-functional teams including Data Engineers, Data Scientists, and Application Developers.
• Troubleshoot production issues and implement performance improvements.
• Follow coding standards, version control, and CI/CD best practices.

Mandatory Skills

• Strong proficiency in Python programming.
• Hands-on experience with PySpark and Apache Spark.
• Strong SQL coding skills.
• Experience with PL/SQL development.
• Experience in Big Data ecosystem.
• REST API development using Flask or Django.
• Experience in developing and consuming Python APIs.
• Knowledge of data processing, ETL, and distributed computing.
• Experience with Git/version control.

Preferred Skills

• Experience with Hadoop ecosystem (Hive, HDFS, YARN).
• Exposure to cloud platforms such as AWS, Azure, or GCP.
• Knowledge of Airflow or other workflow orchestration tools.
• Experience with Docker and Kubernetes.
• Familiarity with Kafka or other streaming technologies.
• Understanding of CI/CD pipelines.

Qualifications

• Bachelor's or Master's degree in Computer Science, Information Technology, or a related field.
• 4–8+ years of experience in Python and Big Data development (can be adjusted based on the role).

Required Experience

• Strong hands-on experience in Python, PySpark, and Apache Spark.
• Extensive experience writing optimized SQL and PL/SQL code.
• Experience developing REST APIs using Flask or Django.
• Experience working with large-scale data processing and ETL pipelines.
• Strong analytical, debugging, and problem-solving skills.

Mandatory Skills: Python, PySpark, SQL Coding, Apache Spark, Big Data, Flask/Django (REST API), PL/SQL, Python APIs.

Job Summary

We are seeking a motivated Data Engineer with strong skills in SQL, Python, and Linux to design, build, and maintain scalable data pipelines and support data-driven decision-making. The ideal candidate should have experience working with large datasets, ETL processes, and relational databases while ensuring data quality and performance.

Key Responsibilities

• Design, develop, and maintain ETL/ELT data pipelines.
• Write optimized SQL queries, stored procedures, and database objects.
• Develop Python scripts for data extraction, transformation, and automation.
• Work in Linux environments to manage scripts, cron jobs, and system processes.
• Monitor and troubleshoot data pipeline failures.
• Ensure data integrity, consistency, and quality across systems.
• Collaborate with data analysts, software engineers, and business stakeholders.
• Optimize database performance and query execution.
• Participate in code reviews and follow best engineering practices.

Required Skills

• Strong proficiency in SQL (joins, subqueries, window functions, CTEs, indexing, query optimization).
• Good programming experience in Python.
• Hands-on experience with Linux commands and shell scripting.
• Understanding of ETL/ELT concepts and data warehousing.
• Knowledge of relational databases such as PostgreSQL, MySQL, Oracle, or SQL Server.
• Familiarity with Git for version control.
• Strong problem-solving and analytical skills.

Job Summary

We are seeking a skilled Data Engineer to design, build, and maintain scalable data pipelines and infrastructure. The ideal candidate should have strong expertise in SQL, Python, Linux, and modern data engineering practices to support data integration, transformation, and analytics.

Key Responsibilities

• Design, develop, and maintain ETL/ELT data pipelines.
• Write efficient and optimized SQL queries for data extraction, transformation, and reporting.
• Develop automation scripts using Python for data processing and workflow optimization.
• Work with Linux environments for deployment, monitoring, and troubleshooting.
• Ensure data quality, integrity, and reliability across data platforms.
• Collaborate with data analysts, software engineers, and business stakeholders to deliver data solutions.
• Monitor, troubleshoot, and optimize data pipelines for performance and scalability.
• Implement best practices for data security, governance, and documentation.

Required Skills

• Strong experience in Data Engineering concepts and ETL/ELT processes.
• Proficiency in SQL, including query optimization and database design.
• Strong programming skills in Python.
• Hands-on experience with Linux commands, shell scripting, and system administration basics.
• Experience with relational databases such as PostgreSQL, MySQL, SQL Server, or Oracle.
• Familiarity with Git/version control.
• Strong analytical and problem-solving skills.

Preferred Skills

• Experience with cloud platforms (AWS, Azure, or GCP).
• Knowledge of Apache Spark, Airflow, Kafka, or similar data engineering tools.
• Experience with data warehousing solutions and big data technologies.
• Understanding of CI/CD pipelines and containerization (Docker/Kubernetes).

Qualifications

• Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field.
• Relevant certifications in cloud or data engineering are an added advantage.

Key Responsibilities

• Own and drive the product roadmap for data and AI-powered features end-to-end

• Translate complex data and AI capabilities into simple, intuitive product experiences for brands and creators

• Collaborate closely with data engineers, ML engineers, and business teams to define and deliver product requirements

• Define product metrics and success criteria — including data quality, model accuracy, and user engagement

• Identify gaps in existing data pipelines and work with engineering to build scalable solutions

• Conduct user research, competitor analysis, and market mapping to inform product decisions

• Prioritise features and manage the product backlog with a strong data-driven approach

• Work with the AI team to evaluate and integrate LLM and GenAI capabilities into the product

Must-Have Skills

• Total experience of 2–3 years with at least 1–2 years in data engineering or data analytics

• Transitioned into or actively pursuing a product management role

• Strong understanding of data pipelines, ETL processes, SQL, and data modelling

• Ability to write and interpret data queries to inform product decisions

• Excellent communication and stakeholder management skills

• Strong product thinking — ability to break down complex problems into simple product solutions

Company Name – Wissen Technology

Group of companies in India – Wissen Technology & Wissen Infotech

Work Location – Whitefield, Bangalore

While you may already know about Wissen and the company history, here is a quick rundown for you.

About Wissen Technology:

·    The Wissen Group was founded in the year 2000. Wissen Technology, a part of Wissen Group, was established in the year 2015.

·    Wissen Technology is a specialized technology company that delivers high-end consulting for organizations in the Banking & Finance, Telecom, and Healthcare domains. We help clients build world class products.

·    Our workforce has highly skilled professionals, with leadership and senior management executives who have graduated from Ivy League Universities like Wharton, MIT, IITs, IIMs, and NITs and with rich work experience in some of the biggest companies in the world.

·    Wissen Technology has grown its revenues by 400% in these five years without any external funding or investments.

·    Globally present with offices US, India, UK, Australia, Mexico, and Canada.

·    We offer an array of services including Application Development, Artificial Intelligence & Machine Learning, Big Data & Analytics, Visualization & Business Intelligence, Robotic Process Automation, Cloud, Mobility, Agile & DevOps, Quality Assurance & Test Automation.

·    Wissen Technology has been certified as a Great Place to Work®.

·    Wissen Technology has been voted as the Top 20 AI/ML vendor by CIO Insider in 2020.

·    Over the years, Wissen Group has successfully delivered $650 million worth of projects for more than 20 of the Fortune 500 companies.

·    We have served client across sectors like Banking, Telecom, Healthcare, Manufacturing, and Energy. They include likes of Morgan Stanley, Goldman Sachs, MSCI, StateStreet, Flipkart, Swiggy, Trafigura, GE to name a few.

Job Title: Azure Fabric Data Engineer / AI Engineer

Experience: 4–8 Years

Location: Pune(Hybrid)

Job Summary

We are seeking Azure Fabric Data Engineers with experience in data engineering, Power BI, and AI to build modern data platforms and AI-driven solutions on Microsoft Fabric.

Key Responsibilities

• Develop ETL/ELT pipelines using Microsoft Fabric.
• Integrate data from multiple enterprise systems into Fabric.
• Build and optimize Lakehouse and Data Warehouse solutions.
• Develop Power BI dashboards and reports.
• Build AI-powered applications, AI Agents, and chatbots using Azure AI Services and Azure OpenAI.
• Collaborate with business and technical teams to deliver scalable analytics solutions.

Required Skills

• Microsoft Fabric
• Data Engineering and ETL/ELT
• SQL, Python, PySpark
• Power BI
• Azure AI Services / Azure OpenAI
• Data Modeling
• Git and Azure DevOps

Preferred: Experience with Financial Services/Capital Markets, Generative AI, RAG, or LLM-based applications.

Data Engineer – Splunk & ELK Stack

Job Summary

We are seeking a skilled Data Engineer with hands-on experience in Splunk, the ELK Stack (Elasticsearch, Logstash, Kibana), and modern data engineering practices. The ideal candidate will design, build, and maintain scalable data pipelines, log analytics platforms, and monitoring solutions to support business intelligence, security, and operational excellence.

Key Responsibilities

• Design, develop, and maintain scalable data ingestion and ETL/ELT pipelines.
• Configure, administer, and optimize Splunk environments for log collection, indexing, searching, and reporting.
• Develop and maintain ELK Stack solutions using Elasticsearch, Logstash, Kibana, and Beats.
• Build dashboards, visualizations, alerts, and reports for infrastructure, application, and security monitoring.
• Integrate data from multiple structured and unstructured sources into centralized analytics platforms.
• Optimize Elasticsearch clusters for performance, scalability, and high availability.
• Troubleshoot data pipeline failures, indexing issues, and system performance bottlenecks.
• Automate deployment and configuration using scripting and Infrastructure as Code where applicable.
• Collaborate with DevOps, Security, Cloud, and Application teams to implement observability and monitoring solutions.
• Ensure data quality, governance, and compliance with organizational standards.
• Document technical designs, operational procedures, and best practices.

Required Skills

• Strong experience with Splunk Enterprise administration and development.
• Hands-on experience with the ELK Stack:
• Elasticsearch
• Logstash
• Kibana
• Beats (Filebeat, Metricbeat, Winlogbeat, etc.)
• Experience building ETL/ELT pipelines and data integration workflows.
• Strong SQL skills and experience with relational databases.
• Experience with Python, Shell scripting, or Java for automation.
• Understanding of log management, monitoring, and observability concepts.
• Experience working with Linux environments.
• Knowledge of REST APIs and data ingestion techniques.
• Familiarity with cloud platforms such as AWS, Azure, or Google Cloud.
• Experience with Git and CI/CD pipelines.

Preferred Skills

• Experience with Kafka, Spark, or other streaming technologies.
• Knowledge of Docker and Kubernetes.
• Experience with Terraform, Ansible, or other Infrastructure as Code tools.
• Understanding of SIEM concepts and security analytics.
• Experience with Prometheus, Grafana, or OpenTelemetry.
• Exposure to big data technologies and distributed systems.

We are looking for a hands-on Data Engineer to help build and manage our data platform for reporting, analytics, and future data science use cases.

The role will involve working with SQL, Python, PySpark, AWS, ETL/ELT pipelines, data warehouses, and BI/reporting tools. Our architecture may use technologies such as ClickHouse, Redshift, AWS Glue, Airflow, Step Functions, Lambda, S3, and CDC-based replication tools based on scale, cost, and operational needs.

Key Responsibilities

·     Design, build, and maintain scalable ETL/ELT data pipelines from multiple databases, applications, and external systems.

·     Build raw, cleaned, and business-ready data layers to support reporting, analytics, and future data science use cases.

·     Write efficient SQL, Python, and PySpark jobs for data ingestion, transformation, validation, and processing.

·     Implement workflow orchestration using Apache Airflow, AWS Glue, Step Functions, or similar tools.

·     Work with data warehouses such as ClickHouse, Redshift, Snowflake, BigQuery, or similar.

·     Support cross-service reporting as the architecture moves towards independent microservice databases.

·     Build reusable reporting tables, aggregates, summaries, and basic data marts.

·     Monitor, troubleshoot, and optimize data pipelines, warehouse queries, and processing jobs.

·     Implement data quality checks for freshness, completeness, consistency, duplicates, and reconciliation.

·     Support BI/reporting needs through tools such as Power BI, Metabase, Superset, Redash, or similar.

·     Apply data governance, access control, security, and PII-handling best practices.

·     Collaborate with engineering, DevOps, product, business, finance, risk, and support teams.

Required Skills

·     Strong expertise in SQL for joins, aggregations, window functions, query optimization, and analytical reporting.

·     Hands-on experience with Python for data processing, automation, validation, and scripting.

·     Working experience with PySpark / Apache Spark for processing large datasets.

·     Good understanding of ETL/ELT pipelines, data warehousing, and data modelling concepts.

·     Experience with workflow orchestration using Apache Airflow, AWS Step Functions, AWS Glue, or similar tools.

·     Experience with AWS data services such as S3, Glue, Lambda, Step Functions, Redshift, DMS, CloudWatch, or similar.

·     Experience with any data warehouse such as ClickHouse, Redshift, Snowflake, BigQuery, or similar.

·     Understanding of relational databases, preferably PostgreSQL.

·     Ability to debug data mismatches, failed pipelines, slow queries, and data quality issues.

·     Exposure to BI tools such as Power BI, Metabase, Superset, Redash, or similar.

Good ownership, problem-solving, communication, and collaboration skills.

Please find below the job description for Senior Azure Fabric Data Architect role with Wissen Technology.

Website and Company profile:

www.wissen.com

LinkedIn Page:

https://www.linkedin.com/company/wissen-technology/

Job Description:

Experience: 8–15+ Years

Location: Wissen Office (Pune/Bengaluru)

Position: 1

Job Summary

We are looking for an experienced Azure Fabric Data Architect to lead the design and implementation of an enterprise data platform on Microsoft Fabric. The role involves architecting scalable data solutions, defining data governance, and enabling AI-driven analytics for a global financial services client.

Key Responsibilities

• Design end-to-end data architecture using Microsoft Fabric.
• Build enterprise Lakehouse, Data Warehouse, and OneLake solutions.
• Define data ingestion, ETL/ELT, governance, security, and performance strategies.
• Lead architecture for AI-powered analytics, AI Agents, and enterprise chatbots using Azure AI services.
• Work with business stakeholders to translate requirements into technical solutions.
• Mentor engineering teams and provide technical leadership.

Required Skills

• Microsoft Fabric (Data Factory, Lakehouse, Data Warehouse, OneLake)
• Azure Data Engineering
• Power BI
• Azure AI Services / Azure OpenAI
• Data Architecture & Data Modeling
• SQL, Python
• Azure DevOps, CI/CD
• Strong stakeholder management and solution design experience

Job Title : Data Engineer – Databricks

Experience : 6+ Years

Location : Noida / Hyderabad / Chennai / Pune / Bengaluru (Hybrid)

Shift : IST (Normal Shift)

Job Summary :

We are seeking an experienced Data Engineer with strong expertise in Databricks, Snowflake, Python, and Spark to build and optimize scalable data pipelines and support AI/ML model deployments. The ideal candidate should have experience working with cloud-based data platforms and preferably possess exposure to the Healthcare domain.

Required Skills :

• Databricks (Preferred)
• Snowflake
• Python
• Apache Spark
• SQL
• Azure Cloud
• Kubernetes
• Apache Airflow
• GitHub & CI/CD Pipelines
• AI/ML Model Deployment
• Data Analytics

Preferred :

• Experience in the Healthcare domain.
• Strong understanding of scalable data engineering architectures and best practices.

Role Overview

We are seeking a Senior SQL Developer & ETL Engineer with 5+ years of experience for a 100% remote opportunity.

Please Note: This is not a pure Data Engineering role. We are looking for a true SQL Specialist. Your core strength must lie in relational database development, schema design, and writing high-performance database logic with Python, ETL, Cloud. SQL mastery and database architecture are the absolute heart of this role.

If you are a database developer who loves diving into query execution plans, refactoring messy stored procedures for 10x performance, and building clean data models from scratch, this role is for you.

Key Responsibilities

1. Database Architecture & Schema Design

• Design, implement, and maintain robust relational database schemas.
• Architect optimal data models for both operational (OLTP) and analytical (OLAP/Data Warehousing) workloads.
• Implement Normalization (3NF) and dimensional modeling (Star/Snowflake schemas) as required.

2. Advanced Database Programmability

• Write, debug, and optimize highly complex Stored Procedures, Functions, Triggers, and Views to handle core business logic at the database level.
• Utilize advanced SQL techniques such as CTEs, Window Functions, and complex analytical queries to solve business problems.

3. Performance Tuning & Indexing

• Analyze query execution plans, identify performance bottlenecks, and implement advanced indexing strategies (B-Tree, Clustered/Non-Clustered, Partitioning).
• Refactor legacy SQL code and manage statistics, locking, and concurrency mechanisms to ensure sub-second response times.

4. Python & Cloud ETL/ELT Pipelines

• Develop, schedule, and maintain scalable data ingestion and transformation pipelines to connect disparate data sources.
• Leverage Cloud Data Platforms alongside modern Python libraries to build efficient data movement workflows.

5. Data Integrity & Governance

• Establish strict database constraints, data validation routines, and automated quality checks to guarantee absolute data accuracy.

Required Technical Skills

• Expert-Level SQL & DB Programmability (5+ Years): Mastery of writing server-side logic (Stored Procedures/Functions) and complex queries in enterprise platforms like PostgreSQL, SQL Server, Oracle, or MySQL.
• Advanced Database Optimization: Deep, under-the-hood understanding of database engines, execution plans, indexing strategies, and concurrency/locking control.
• Python for Data Engineering (3+ Years): Proficient in writing clean, modular Python scripts for API integration, data manipulation, and ETL processing (using libraries like Pandas, SQLAlchemy, or custom database connectors).
• Cloud Data Experience: Hands-on experience working with, migrating to, or developing within major cloud environments (AWS, Azure, GCP) and modern cloud data warehouses (Snowflake, BigQuery, or Redshift).
• Data Modeling Methodologies: Practical experience designing Star/Snowflake schemas, handling Slowly Changing Dimensions (SCD), and balancing normalization vs. denormalization.

Remote & Soft Skills

• Legacy Refactoring Mindset: You genuinely enjoy opening up a massive, poorly optimized 500-line legacy stored procedure and refactoring it for maximum efficiency.
• Autonomous Execution: Proven ability to manage your own time, architecture tasks, and deliverables without micromanagement in a fully remote setup.
• Asynchronous Communication: Exceptional written and verbal English communication skills to collaborate seamlessly across time zones.

Nice-to-Haves

• Experience migrating legacy on-premise infrastructure and stored procedures to modern cloud data warehouses.
• Familiarity with workflow orchestration tools like Apache Airflow or Prefect.
• Hands-on experience with dbt (data build tool) for in-warehouse transformations.

About Marseer AI

Marseer AI (www.marseerai.com) is a modular AI activation platform built for DTC and retail e-commerce brands. damStack is Marseer AI's Snowflake-native, dbt-driven data and marketing activation product. It follows a Listen -> Reflect -> React architecture across composable applications, each running inside a customer's own Snowflake data warehouse.

Role Overview

We are looking for a Senior Data Engineer to join the damStack engineering team at Marseer AI. You will design and implement data pipelines, dbt transformation models, and Snowflake-native data products for retail and e-commerce brands. This is a hands-on, high-ownership role across ingestion, transformation, activation workflows, and client onboarding.

What You Will Do

- Design, build, and maintain dbt models across staging, intermediate, and mart layers for customer identity, segmentation, journey orchestration, and activation outputs.

- Implement incremental dbt models, snapshots, and tests to ensure data freshness, accuracy, and reliability.

- Contribute to damStack's Open Schema and unified customer_360 semantic layer.

- Build and refine SQL-based rule engines in Snowflake for priority resolution, frequency capping, and activation orchestration.

- Configure and manage Airbyte connectors for bidirectional data sync such as MongoDB to Snowflake and Snowflake to Klaviyo.

- Build and maintain Dagster pipelines to orchestrate dbt runs, Airbyte sync jobs, and cross-pipeline dependencies.

- Support integration of external marketing platforms into the damStack data layer.

- Work directly with client brands to understand data sources, schemas, and business requirements.

- Translate client data into damStack's standardized activity schema and entity resolution framework.

- Troubleshoot data quality and integration issues in client environments.

- Contribute to single-tenant Snowflake deployments, data quality tests, monitors, alerting, and technical design documentation.

Requirements

- 5+ years of professional experience in data engineering.

- Advanced proficiency in dbt Core or Cloud, including incremental models, snapshots, tests, macros, and multi-layer DAG design.

- Strong hands-on Snowflake experience, including DDL/DML, Snowflake Tasks, query optimization, and multi-tenant data architecture.

- Expert-level SQL, including window functions, CTEs, complex joins, and performance tuning.

- Ability to communicate pipeline designs and technical decisions clearly to technical and non-technical stakeholders.

Strongly Preferred

- Experience with Airbyte or comparable ELT / connector platforms.

- Familiarity with Dagster or similar orchestration tools such as Airflow or Prefect.

- Prior experience in a SaaS or data product company shipping reusable, multi-tenant data infrastructure.

- Understanding of identity resolution patterns, surrogate key architectures, customer data platforms, or experimentation / A/B testing data pipelines.

Good to Have

- Familiarity with Klaviyo or other marketing activation / ESP platforms.

- Experience with MongoDB or document-store integrations.

- Prior experience in retail or e-commerce data domains.

What We Are Looking For

- Availability for US business hours, with at least 5 hours of overlap with America/New_York.

- Ownership mindset, attention to schema naming, test coverage, documentation, and reliable pipelines.

- Collaborative, structured communication with a distributed team.

What We Offer

- Compensation range: INR 20-30 LPA.

- Fully remote role; work from anywhere in India, with Hyderabad-based candidates preferred.

- High-ownership engineering work on Snowflake, dbt, Airbyte, and Dagster.

- Direct exposure to real DTC and retail e-commerce data problems at scale.

- A small, senior team where your contributions are visible.

Join Hutech Solutions – Innovate, Lead, and Transform!

We are a global AI-driven software services and product engineering powerhouse, founded and led by

visionary technology leaders from Walmart. We are redefining the future of technology by building

next-gen solutions that empower businesses across Banking, Finance, eCommerce, and Logistics

industries.

At Hutech, we don’t just build software—we create impact. Our culture fosters innovation, creativity,

and continuous learning, enabling our team to push boundaries and solve real-world challenges using

cutting-edge AI tools and techniques.

Position Summary

We are looking for a skilled Data Engineer with strong expertise in Amazon Redshift, advanced SQL,

and data modelling to design, build, and optimize scalable data platforms on AWS. The ideal

candidate will play a key role in developing reliable data pipelines, enforcing data quality standards,

and enabling analytics and reporting across the organization.

Key Responsibilities

● Design, build, and optimize data models (fact/dimension, star/snowflake schemas) with a

strong focus on performance and scalability.

● Develop and maintain complex SQL queries in Amazon Redshift for analytics, reporting, and

downstream consumption.

● Optimize Redshift performance using distribution styles, sort keys, query tuning, and workload

management (WLM).

● Build and orchestrate scalable data pipelines using AWS Glue, Amazon EMR, Apache Spark,

and Airflow.

● Implement data quality checks, validation rules, and monitoring frameworks to ensure

accuracy and consistency.

● Work closely with analytics, BI, and business teams to translate requirements into robust data

solutions.

● Manage and optimize data storage and movement using Amazon S3.

● Ensure best practices in data security, governance, and documentation.

● Troubleshoot data issues and provide root-cause analysis and long-term fixes.

Required Qualifications

● Bachelor’s degree in Computer Science, Engineering, or a related field

● 3–5 years of hands-on experience in data engineering

● Strong advanced SQL skills (mandatory) with deep experience in Amazon Redshift

● Proven expertise in data modeling for analytical workloads

● Strong understanding of AWS data services, including: Amazon Redshift, AWS Glue, AmazonS3, Amazon EMR

● Experience with data pipeline and workflow orchestration tools such as Apache Airflow

● Hands-on experience with Apache Spark

● Proficiency in at least one programming/scripting language such as Python, Java, or Scala

● Solid understanding of data quality, validation, and best practices

Nice to have

● Experience designing large-scale analytics and reporting platforms

● Familiarity with BI tools and downstream analytics use cases

● Experience with cost optimization and performance tuning on AWS

● Exposure to CI/CD for data pipelines

Key Skills

● Amazon Redshift (SQL – Advanced)

● Data Modeling (Analytics-focused)

● AWS (S3, Glue, EMR)

● Apache Airflow

● Apache Spark

● Python / Java / Scala

● Data Quality & Optimization

Location: Bangalore (Hybrid/Onsite)

Experience: 5–8 Years

Work location -Manyata Tech park

Job Description

We are seeking a skilled GCP Data Engineer with 5–8 years of experience in designing, developing, and maintaining scalable data pipelines and cloud-based data solutions. The ideal candidate should have strong expertise in Google Cloud Platform (GCP), Python, PySpark, SQL, and Data Engineering concepts.

Key Responsibilities

• Design, build, and optimize scalable ETL/ELT data pipelines.
• Develop and maintain data processing solutions using Python and PySpark.
• Work with large-scale structured and unstructured datasets.
• Implement data ingestion, transformation, and data quality frameworks.
• Build and manage data solutions on Google Cloud Platform (GCP).
• Develop and optimize complex SQL queries, stored procedures, and data models.
• Collaborate with business stakeholders, data analysts, and cross-functional teams to understand data requirements.
• Monitor, troubleshoot, and improve data pipeline performance and reliability.
• Ensure data governance, security, and compliance standards are followed.
• Support data warehousing and analytics initiatives.

Required Skills

• 5–8 years of experience in Data Engineering.
• Strong programming experience in Python.
• Hands-on experience with PySpark and distributed data processing.
• Strong expertise in SQL and database performance tuning.
• Experience with Google Cloud Platform (GCP) services such as:
• BigQuery
• Cloud Storage
• Dataflow
• Dataproc
• Cloud Composer
• Pub/Sub
• Experience in designing ETL/ELT workflows.
• Knowledge of data warehousing concepts and dimensional modeling.
• Experience with version control tools such as Git.
• Strong problem-solving and analytical skills.

Preferred Skills

• Experience with CI/CD pipelines and DevOps practices.
• Exposure to orchestration tools like Airflow/Cloud Composer.
• Experience working in Agile/Scrum environments.
• Knowledge of streaming data processing and real-time data pipelines.

Educational Qualification

• Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related field.

Role: Data Engineer

 Experience: 5+ Yrs

Type: Hybrid (3 Days a week)

Location: Chennai, Bangalore, Hyderabad, Pune, Kolkatta

End Client: Cognizant

Contract Duration: 6 Months

Shift:- 10 AM - 7 PM IST

Must Have: 

* Strong experience in Python

* Expertise in Data Engineering Frameworks

* Hands-on experience with ETL processes

* CI/CD

* Experience working on GCP (Google Cloud Platform)

Solid Fundamentals and exceptional problem-solving skills

Solid and fluent understanding of algorithm and data structures

Proficiency in Scala + Spark

Experience Range: 3 to 7 Years

Requirement Some or all of them – because we believe intelligent people can pick up whatever they need in a short period of time. You just need to prove that you can:

Excellent programming skills and knowledge of Java / Scala

Excellent software design, problem solving and debugging skills

Experience with modern Big data technologies such as Spark, NoSQL, Cassandra, Kafka, Map Reduce, Hadoop ecosystem is a must have

Experience with data analytics and ability to mine data to obtain insights is much appreciated

Scrum Master

Bangalore (Marathahalli)

Key Responsibilities

• Lead digital transformation initiatives for global clients within the data engineering domain, aligning technical solutions with business objectives
• Act as a bridge between business and technical teams to gather, analyze, and translate requirements into user stories and acceptance criteria
• Own and manage product backlogs, ensuring continuous grooming and prioritization aligned with client goals
• Facilitate Agile/Scrum ceremonies including daily stand-ups, sprint planning, and retrospectives
• Collaborate with cross-functional teams (Data Engineering, QA, DevOps, and stakeholders) to ensure seamless delivery
• Drive Agile best practices and continuous improvement in processes, timelines, and product quality
• Track and report team performance metrics, sprint progress, and release planning

Required Qualifications

• 8+ years of experience in IT services with a mix of business analysis and Agile delivery roles
• Strong experience working with data engineering teams / data platforms
• Proven experience in delivering client-facing digital products and managing complex stakeholder environments
• Hands-on experience with Agile tools such as JIRA, Confluence, and backlog management platforms
• Strong analytical, communication, and facilitation skills with the ability to balance business and technical priorities