50+ ETL Jobs in India

Job Summary

We are seeking an experienced Integration Support Engineer with 2+ years of experience in enterprise integration and production support. This role will act as the complete owner of the customer support board — managing, triaging, and resolving support tickets end to end while coordinating with engineering, product, and implementation teams. The ideal candidate has strong hands-on experience with Boomi or another iPaaS platform, deep working knowledge of databases and REST APIs, and a track record of rigorous testing and validation. Experience with Intapp Integration Builder is a plus.

Work Hours: 6:00 PM – 5:00 AM, with flexible breaks during the shift.

Key Responsibilities

• Own the customer support board end to end — triaging incoming tickets, managing priority and status, and driving each issue through to resolution.

• Serve as the frontline point of contact for customers on integration issues, providing clear, timely communication throughout the life of a ticket.

• Debug and troubleshoot integrations built on Boomi or other iPaaS platforms, performing root-cause analysis and resolving incidents within SLAs.

• Write, optimize, and troubleshoot SQL queries against production and staging databases for data validation, reconciliation, and issue investigation.

• Work extensively with REST/SOAP APIs, FTP/SFTP, and data formats such as JSON and XML to diagnose and resolve integration failures.

• Test and validate reported issues, including reproducing defects, verifying fixes, and confirming resolution before closing tickets.

• Participate in integration design and enhancement work, contributing to the build and improvement of existing integration processes.

• Take up new development and change requests, following SDLC practices — design, build, test, and deploy — to release changes safely to production.

• Work across internal teams — engineering, product, and implementation — to triage incoming issues, determine ownership, and coordinate resolution.

• Collaborate closely with engineering, product, and implementation teams to resolve both technical issues (database, integration, and server-side) and business/process issues.

• Stay flexible and continuously build cross-functional knowledge, since resolving tickets often requires learning new systems and business workflows on the fly.

• Maintain technical documentation, support guides, and troubleshooting procedures.

Qualifications:

• Bachelor's degree in Computer Science, IT, or a related field.

• 2+ years of experience in integration engineering, integration support, or a similar role.

• Hands-on experience with Boomi or another iPaaS platform.

• Strong, demonstrable skills in SQL and database troubleshooting — this role requires day-to-day, hands-on database work.

• Strong REST/SOAP API knowledge, including testing, debugging, and validating API-based integrations.

• Solid experience with structured testing and validation practices for integration issues.

• Good understanding of integration patterns, cloud technologies, and production support.

• Strong analytical, communication, and stakeholder-management skills.

• Ability to work flexibly across engineering, product, and implementation stakeholders, and to quickly learn new systems and domains.

• Willingness and ability to work the required shift: 6:00 PM – 5:00 AM.

Preferred Skills

• Boomi or relevant iPaaS certification.

• Experience with Intapp Integration Builder.

• Knowledge of Azure Functions, Logic Apps, and other Azure services.

• Experience with monitoring, logging, and integration observability.

What We Offer

Competitive salary and benefits, work-from-home flexibility, a dynamic work environment, and opportunities for professional growth while working on enterprise-grade integrations.

JD:

We are looking for a skilled Axiom Technical Analyst / Developer to support and enhance regulatory reporting platforms.

The ideal candidate will have strong hands-on experience with Axiom, solid SQL skills, and a good understanding of ETL processes. Exposure to Python is a plus.

This role involves close collaboration with Finance, and Data Engineering teams to ensure accurate, timely, and compliant data reporting.

About the Role

We are looking for a motivated Data Engineer with 2+ years of professional experience to join our team. You will be responsible for designing, developing, and maintaining scalable data pipelines and cloud-based data solutions while taking ownership across the full software development lifecycle.

The ideal candidate will have strong experience in modern data engineering practices, cloud platforms, and marketing/advertising data integrations such as Google Ads, Meta Ads, and analytics platforms.

This role is suited for someone who enjoys solving complex data challenges, building reliable systems, and working in a fast-paced environment.

Key Responsibilities

• Design, build, and maintain scalable and reliable ETL/ELT data pipelines.
• Develop and optimize data models, transformations, and warehouse solutions for analytics and reporting.
• Work with marketing and advertising datasets from platforms such as Google Ads, Meta Ads, Google Analytics, and similar ecosystems.
• Integrate APIs, third-party systems, and cloud-native services into data workflows.
• Optimize complex SQL queries and improve pipeline performance and reliability.
• Implement data quality checks, monitoring, and observability across pipelines.
• Collaborate with cross-functional teams including product, analytics, and engineering teams to deliver data-driven solutions.
• Contribute to software engineering best practices including Git workflows, CI/CD pipelines, testing, and documentation.
• Participate in architecture discussions and help improve data platform standards and best practices.
• Build and maintain solutions within Google Cloud Platform (GCP) environments.

Qualifications

• 2+ years of professional experience in Data Engineering or related fields.
• Strong proficiency in Python programming.
• Advanced SQL skills with experience in query optimization and data warehousing concepts.
• Experience working with marketing and advertising platforms such as Google Ads, Meta Ads, Google Analytics, DV360, or similar platforms.
• Experience working with AI-assisted coding and development tools such as Claude Code, Cursor, or similar platforms
• Hands-on experience leveraging AI-based development tools to accelerate data engineering implementation and automation
• Familiarity with AI-powered coding assistants and agentic development workflows
• Understanding of marketing data pipelines, attribution reporting, campaign analytics, or customer analytics is highly desirable.
• Hands-on experience with at least one major data engineering technology such as Airflow, Spark, Kafka, Databricks, or similar frameworks.
• Strong experience with Google Cloud Platform (GCP) services such as BigQuery, Cloud Run, Cloud Functions, GCS, or Composer.
• Familiarity with APIs, Linux environments, CLI tools, Git, and CI/CD workflows.
• Strong understanding of data pipeline design, orchestration, monitoring, and troubleshooting.
• Excellent communication, collaboration, and problem-solving skills.
• Ability to work independently and contribute across multiple technical domains.

Who are we?

Inflexion Analytics is a team of data science and analytics consultants based in London, UK, and Bangalore India. Founded in 2015, we have built a strong track record and foundation serving demanding clients. We are now looking to achieve significant growth and become a leading specialist consultancy in the data science field.

What we do?

We help businesses to improve performance through better insight and decision-making. To achieve this, we offer services across the data science value chain including; data strategy, data engineering, data insights, and visual analytics. We work with a global client base, including clients based in the US, UK, Europe, and Australia. 

Data Engineer

Data Lakehouse & Platform Engineering 

About the Role

We are hiring Data Engineer to own the lifecycle of our enterprise Data Lakehouse platform. We are looking for engineers who think in systems, make platform-level design decisions, and can build and operate a production-grade, multi-source lakehouse from the ground up, covering ingestion through consumption across a complex, multi-cloud source landscape.

You will be the technical authority for a platform that consolidates data from 18+ enterprise products (Costpoint, GovWin, Specpoint, Vantagepoint, and others) into a governed, medallion-architected data lake on AWS S3 with Apache Iceberg table format, orchestrated via AWS Step Functions, and queryable through AWS Athena and Trino. This role is end-to-end: you own ingestion, transformation, quality, orchestration, ML data supply, and BI consumption.

Key Responsibilities

•      Architect and evolve the full medallion lakehouse — Bronze, Silver, and Gold layers — on AWS S3 with Apache Iceberg; own schema design, partitioning, compaction, and retention policies.

•      Design and implement scalable Glue ETL (PySpark) pipelines for bronze_to_silver and silver_to_gold transformations, incorporating dbt for SQL-layer transformations where appropriate.

•      Own and extend CDC ingestion via Fivetran; manage schema evolution, connector health, and sync reliability across 18+ source products.

•      Build and maintain AWS Step Functions state machines and EventBridge schedules for end-to-end pipeline orchestration; implement Lambda-based quality and drift monitors.

•      Govern the Glue Catalog and Lake Formation policies; enforce column-level security, row-level access controls, and audit logging to meet SOC2 and regulatory requirements.

•      Architect the query layer — optimize Athena workgroups and partition pruning; plan and execute Trino-on-EKS deployment for sub-second analytics workloads.

•      Partner with data science teams on SageMaker data supply: feature engineering pipelines, training dataset preparation, and model registry integration.

•      Implement real-time and near-real-time streaming solutions using Kafka or Kinesis where sub-13-minute latency is required.

•      Lead platform modernization initiatives: evaluate emerging formats (Iceberg vs. Delta Lake vs. Hudi), tooling, and cost optimization strategies.

•      Establish and enforce data engineering best practices: code reviews, CI/CD for pipeline code, IaC (Terraform / CloudFormation), and incident response runbooks.

•      Mentor and level up junior and mid-level data engineers; define team standards for pipeline design, testing, and documentation.

Required Qualifications

•      Software or data engineering experience, with at least 4 years in an architect or technical lead capacity designing large-scale cloud data platforms.

•      Deep, hands-on expertise with AWS data services: S3, Glue (PySpark ETL), Athena, Step Functions, Lambda, EventBridge, Lake Formation, SageMaker, and CloudWatch.

•      Production experience with Apache Iceberg (or Delta Lake / Hudi) table formats — compaction, snapshot management, schema evolution, and time travel.

•      Strong PySpark and Python skills; ability to write, review, and optimize distributed data processing jobs at scale.

•      Hands-on experience with CDC-based ingestion platforms (Fivetran, Debezium, or equivalent) across heterogeneous source systems.

•      Proven experience designing and implementing medallion (Bronze/Silver/Gold) or equivalent multi-hop lakehouse architectures.

•      Experience with data pipeline orchestration: AWS Step Functions, Apache Airflow, or equivalent; event-driven pipeline design patterns.

•      Strong SQL skills; experience with Athena, Trino, Presto, or equivalent query engines for large-scale analytical workloads.

•      Familiarity with data governance tooling: catalog management (Glue Catalog, Apache Polaris/Iceberg REST), data lineage, access controls, and audit frameworks.

•      Experience with Infrastructure as Code (Terraform or CloudFormation) for data platform provisioning and drift management.

•      Solid understanding of dimensional modeling, schema design (star/snowflake), and data normalization for BI and analytics workloads.

•      Bachelor's degree in Computer Science, Engineering, or a related field; or equivalent professional experience.

Preferred Qualifications

•      Experience operating Trino or PrestoDB on Kubernetes (EKS); tuning for sub-second query latency and multi-tenant workloads.

•      Familiarity with streaming platforms (Kafka, Kinesis, or Pub/Sub) and real-time lakehouse patterns.

•      Experience with Apache Polaris or other Iceberg REST catalog implementations.

•      Exposure to SageMaker MLOps pipelines, Model Registry, and feature store patterns for ML data supply.

•      Experience with dbt (data build tool) for SQL-layer transformation and documentation in lakehouse environments.

•      Government contracting or ERP domain knowledge (Costpoint, Deltek, Oracle, or similar enterprise platforms) is a strong plus.

•      AWS certifications: Data Engineer Associate, Solutions Architect Professional, or equivalent.

What You Will Build

You will be a founding architect of a strategic, cross-product data platform that serves 18+ enterprise applications and their analytics, ML, and AI workloads. The decisions you make on schema, storage format, query layer, governance, and orchestration will shape the data foundation of the company for years. This is a high-impact, high-ownership role with direct visibility to senior leadership.

Key Responsibilities

1. Solutioning & Proposal Development

•   Partner with Senior SMEs and Practice Leads to design end-to-end Data & AI solutions for client pursuits — contributing to structure, content, and commercial framing.

•   Build client proposals, solution documents, and program structures that are well-organized, accurate, and ready to use without significant rework.

•   Translate client requirements into structured, outcome-oriented learning journeys — adoption, capability uplift, and measurable business outcomes, not just module lists.

•   Support customized offerings across Data Engineering, AI / ML, and GenAI and Agentic AI tracks; help assemble pursuits from existing accelerators rather than rebuilding from scratch.

2. Client Engagement Support

•   Participate in client discussions, discovery calls, and requirement-gathering sessions — capture context with the rigour that makes the next conversation sharper.

•   Convert business needs into solution frameworks and delivery models with guidance from Senior SMEs; document customer priorities so Practice and Sales can act on them.

•   Support pitch decks, case studies, and success stories — buyer-specific, visually clean, and aligned to how the customer thinks about their own problem.

•   Stay engaged through the proposal cycle and handoff to delivery; ensure no requirement gets lost between discovery and execution.

3. Content & Program Structuring

•   Assist in designing curriculum outlines, learning journeys, and hands-on lab structures that hold up against real-world enterprise contexts.

•   Work with internal and external SMEs to ensure content aligns with current industry trends, real use cases, and business outcomes — not generic technology overviews.

•   Maintain a library of reusable program structures, slide assets, and case study inserts; flag gaps in the existing content library proactively.

4. Research & Market Intelligence

•   Track trends across the AI / GenAI / LLM ecosystem and Data Engineering & Analytics — translate findings into usable inputs for outreach, pitching, and offering design.

•   Identify new solution opportunities and product ideas based on market signals, customer asks, and competitor moves.

•   Benchmark StackRoute's offerings against competitors; surface gaps and differentiation angles for Senior SMEs and Practice Leads to act on.

5. Internal Collaboration

•   Work fluidly with Delivery, Sales, and external SMEs to ensure solutions designed on paper actually work in delivery — surface feasibility risks early, not late.

•   Coordinate inputs across Practice teams during pursuit cycles; hand off to delivery with documentation that captures customer commitments and success metrics.

Must Have Technical & Functional Skills

•   Good understanding of terminologies in Data Engineering (ETL, pipelines, data lakes), Data Analytics & BI concepts, and Machine Learning fundamentals, AI tools ( not technical expertise but L1-L2 knowledge should be present from application standpoint).

•   Awareness of GenAI / LLM vocabulary — prompt engineering, RAG, APIs — with enough depth to hold a credible first conversation with a technical stakeholder.

•   Strong PowerPoint skills (client-ready decks), Excel for effort estimation and costing basics, and structured documentation — proposals, SoWs, one-pagers.

•   Strong problem-solving and structured thinking — breaks complex requirements into clear, communicable solutions.

•   Comfortable communicating with both technical and non-technical stakeholders; good storytelling and presentation instincts; understanding of L&D context is a plus.

Qualifications & Experience

Required:

•   5-15 years in the education products, or in solutioning, pre-sales, or consulting roles with exposure of 3-5 years in Data/AI/Analytics.

•   Bachelor's or Master's in Computer Science, Data Science, Engineering, or a related discipline.

Nice to Have competences:

•   Prior pre-sales, proposal writing, or design development experience.

•   Certifications in cloud, data, or AI platforms (AWS, Azure, GCP, or model-provider certifications).

Core Competencies

·      Structured Thinking: Organises ambiguous client and technical inputs into logical, buyer-relevant narratives. Builds proposals that flow from problem to solution.

·      Solution Articulation: Translates Data & AI capabilities into crisp, persona-specific stories. Adapts the pitch for a CTO, L&D Head, or BU Head without losing substance.

·      Research & Synthesis: Gathers and distils large volumes of information into sharp, usable outputs. Knows what to include and what to leave out.

·      Written Communication: Writes a tight brief, a clean slide, and a clear email. Adapts register from internal working notes to buyer-facing collateral.

·      Curiosity & Learning Agility: Picks up new tools, concepts, and sectors quickly. Tracks AI / GenAI shifts proactively rather than waiting to be told what to read.

·      Bias for Action: Ships a useful 10-slide deck on time rather than a polished 20-slide deck that's late. Comfortable with iteration over perfection.

Job Title: Senior Data Tester

Location : Hyderabad

Mode: Hybrid

Notice Period: Immediate Joiner

Key Responsibilities:

• 8+ years of experience in ETL/data testing.
• Design, implement, and execute data validation test plans and test cases.
• Understanding of data modelling and data governance principles.
• Experience with test automation frameworks and scripting (e.g., Python, Shell)Conduct thorough ETL testing, including data extraction, transformation, and loading.
• Validate data integrity across various sources and destinations (data lakes, warehouses, etc.)
• Perform data reconciliation and analysis to identify inconsistencies or data quality issues.
• Develop and maintain automated data testing frameworks using SQL or scripting languages.
• Strong experience with SQL and writing complex queries for data validation.
• Knowledge of data warehouse concepts and testing tools. Experience with ETL tools (e.g., Informatica, Talend, SSIS, etc.)
• Familiarity with cloud platforms (Azure, GCP) and modern data tools (e.g., Snowflake, Big Query).
• GCP is mandatory. Experience in Agile development and working within cross-functional teams.
• Exposure to BI tools (Power BI, Tableau, Looker)
• Familiarity with CI/CD pipelines and version control systems like Git ISTQB or equivalent testing certifications.

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.

Hiring for Data Analyst

Exp : 5 - 7 yrs

Edu : BE/B.Tech

Work Location : Noida WFO

Skills :

Expertise in SQL Server, including database design, performance tuning, query optimization, and security.

Hands-on experience developing ETL solutions using SSIS, Azure Data Factory (ADF), and Python.

Skills Referential (Required knowledge, skills and abilities)

Technical Skills:

Python

Pyspark

SQL

ETL Aws, Azure, gcp

Must have experience in Java

Must have experience in Spark

Must have experience in ETL coding

Strong expertise in coding

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.

Design, build, and maintain end-to-end data pipelines to ingest, process, and transform data from files, streams,

APIs, and relational/non-relational databases into Snowflake.   Develop and optimize ELT/ETL pipelines using Snowflake SQL,

Snowpipe, Streams & Tasks, and cloud-native orchestration tools.   Implement scalable data models and schemas (staging, curated, and consumption layers) to support analytics and reporting use cases.   Develop transformations and business logic using SQL and Python, including Snowflake UDFs and stored procedures.   Optimize Snowflake performance and cost through query tuning, warehouse sizing, clustering, and resource management.   Integrate Snowflake with cloud storage and services across AWS and Azure (e.g., object storage, data integration, and mess

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 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)

We are looking for a Data Engineer with at least 1 year of hands-on experience building solutions on Snowflake. The candidate should be comfortable designing, building, and managing reliable data pipelines that move data from multiple sources into a central data platform.

Responsibilities

• Build and maintain data pipelines for ingesting, transforming, and loading data into Snowflake
• Design scalable data models, schemas, tables, and views in Snowflake
• Develop ETL/ELT workflows using SQL, Python, or data orchestration tools
• Integrate data from APIs, databases, files, and third-party platforms
• Monitor pipeline performance, failures, data quality, and freshness
• Optimize Snowflake queries, warehouses, storage, and compute usage
• Implement incremental loads, change data capture, and scheduled workflows
• Work with engineering and business teams to understand data requirements
• Maintain documentation for pipelines, datasets, and data transformations

Requirements

• 1+ year of hands-on experience working with Snowflake
• Strong SQL skills and experience writing complex queries
• Experience building and managing ETL or ELT data pipelines
• Knowledge of data warehousing concepts, dimensional modelling, and data quality
• Experience with Python or another scripting language
• Familiarity with orchestration tools such as Airflow, Dagster, Prefect, dbt, or similar
• Understanding of APIs, relational databases, file formats, and cloud storage
• Ability to troubleshoot pipeline failures and performance issues
• Strong analytical, problem-solving, and communication skills

Good to Have

• Experience with dbt and Snowflake Tasks, Streams, Snowpipe, or Dynamic Tables
• Knowledge of AWS, Azure, or Google Cloud
• Experience with Kafka or other streaming platforms
• Familiarity with CI/CD, Git, monitoring, and data governance practices
• Experience integrating ERP, finance, or operational systems

Role Summary

We are hiring a Data Engineer / ML Data Pipeline Engineer to build and operate the data backbone of the Enterprise AI platform:

What You'll Own

• Ingestion & ETL/ELT pipelines for heterogeneous project folders (PDF drawings, SVG files, IFC models, BBS.json bar-bending-schedule data, Excel exports, and AI agent output JSON).
• AWS-based data architecture: S3 raw/staging/curated/outputs structuring, partitioning, versioning, and lifecycle management; querying via Athena/Glue and warehousing via Redshift or Snowflake as needed.
• Data validation frameworks: GUID cross-referencing between SVG and BBS data, schema enforcement, duplicate/orphan detection, reference integrity checks, and structured validation reporting.
• Agent run logging & observability: designing the database schema and pipelines that track every AI agent run (inputs, outputs, status, errors, cost, retries, reviewer feedback).
• AI Factory monitoring dashboards: operational dashboards (failure rates, retries, latency, data quality) and business dashboards (throughput, cost per run, rework rate) for Power BI/QuickSight or equivalent.
• ML data pipeline support: dataset preparation, labeling/annotation workflows, human-in-the-loop review tooling, and dataset versioning for models that classify or QC drawing issues.
• APIs: designing and building FastAPI/Flask endpoints to trigger validation runs and expose agent processing status to internal tools.
• Data quality & testing discipline: idempotent pipelines, quarantine/reject handling, regression and reconciliation testing, and root-cause debugging when pipelines or query performance degrade in production.

Key Skills — Non-Negotiable (Must-Have, Strong Level)

• Python — production-grade scripting: file/folder handling, JSON/schema processing, clean error handling, not just notebook-level scripting.
• SQL — strong hands-on ability, including GROUP BY/HAVING for duplicate detection, window functions, and daily aggregate/rate calculations (e.g., success-rate queries).
• AWS S3 data handling — practical experience structuring buckets for raw/staging/curated data, versioning, and avoiding overwrite issues at scale.
• Data validation — demonstrable experience building validation logic (set comparisons, duplicate/missing detection, structured pass/fail reporting), not just "I write assertions."
• ETL/ELT pipeline design — end-to-end ownership of at least one pipeline: source → transform → storage → validation → monitoring → business outcome, with clear articulation of what they personally built.
• Query/warehouse engine judgment — working knowledge of when to use Athena vs. Redshift vs. Snowflake (or equivalent), partitioning, clustering, sort/distribution keys, and storage format trade-offs (Parquet vs. JSON vs. CSV).

Key Skills — Good to Have

• Dashboarding — Power BI / QuickSight (or equivalent) fact/dimension table design, KPI cards, drill-downs; medium-to-strong level is a plus but trainable.
• FastAPI / Flask — building real endpoints with request/response schemas and basic error handling; especially valuable for validation-trigger and agent-status APIs.
• ML data pipeline experience — dataset labeling, annotation platform design, train/test/validation splitting, dataset versioning; strong on the pipeline/data side rather than model training itself.
• Human-in-the-loop / review tooling — experience building or contributing to browser-based labeling/review platforms (session persistence, label schema, export formats).
• Large-scale metadata querying — experience making file discovery fast across large volumes (1,000+ projects, thousands of files each) via metadata index tables, event-based ingestion, or catalog tools like AWS Glue.

Job Summary

We are seeking a highly skilled GCP Data Engineer with strong expertise in Google Cloud Platform (GCP), Python, ETL, and modern data engineering technologies. The ideal candidate should have hands-on experience designing and building scalable data pipelines using BigQuery, Dataflow, Pub/Sub, Airflow, and modern data lake technologies such as Apache Iceberg or Delta Lake.

Key Responsibilities

• Design, develop, and maintain scalable ETL/ELT data pipelines on Google Cloud Platform.
• Build and optimize data processing workflows using Python and Google Cloud Dataflow (Apache Beam).
• Develop and manage large-scale analytical data models in BigQuery.
• Implement event-driven data ingestion using Google Cloud Pub/Sub.
• Create, schedule, and monitor workflows using Apache Airflow and Autosys.
• Design and implement modern data lake architectures using Apache Iceberg or Delta Lake.
• Optimize query performance, storage, and compute costs in GCP.
• Ensure data quality, governance, security, and compliance across data platforms.
• Collaborate with Data Scientists, Analysts, and Application teams to deliver scalable data solutions.
• Troubleshoot production issues and continuously improve pipeline reliability and performance.

Mandatory Skills

• Strong hands-on experience with Google Cloud Platform (GCP).
• Proficiency in Python programming.
• Experience in designing and implementing ETL/ELT pipelines.
• Strong knowledge of BigQuery.
• Experience with Google Cloud Dataflow (Apache Beam).
• Experience with Google Cloud Pub/Sub.
• Hands-on experience with Apache Airflow.
• Experience in job scheduling using Autosys.
• Experience with modern table formats such as Apache Iceberg or Delta Lake.
• Strong SQL and data modeling skills.

Preferred Skills

• Experience with Cloud Storage, Dataproc, Cloud Composer, and Cloud Functions.
• Knowledge of CI/CD pipelines and DevOps practices.
• Experience with Docker and Kubernetes.
• Familiarity with Git and Agile/Scrum methodologies.
• Knowledge of data warehousing and dimensional modeling.
• Exposure to streaming and real-time data processing.

Qualifications

• Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related field.
• 4–8+ years of experience in Data Engineering with hands-on expertise in GCP technologies.

Required Experience

• Strong experience in developing enterprise-grade data pipelines using Python and GCP.
• Hands-on experience with BigQuery, Dataflow, Pub/Sub, and Airflow.
• Experience scheduling and monitoring batch workflows using Autosys.
• Experience implementing modern data lake architectures using Apache Iceberg or Delta Lake.
• Strong understanding of ETL best practices, performance tuning, and data optimization.
• Excellent analytical, troubleshooting, and problem-solving skills.

Mandatory Skills

• Google Cloud Platform (GCP)
• Python
• ETL
• BigQuery
• Autosys
• Apache Airflow
• Google Cloud Pub/Sub
• Google Cloud Dataflow (Apache Beam)
• Apache Iceberg / Delta Lake
• SQL & Data Modeling

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.

Key Responsibilities

Data Modeling & Power BI Development

Design, develop, and maintain high-performance Power BI dashboards and reports.

Build robust, scalable data models using **advanced DAX (Data Analysis Expressions)** to implement complex business logic, time-intelligence calculations

Optimize existing reports and data models for speed, efficiency, and scalability.

Advanced Data Extraction & Transformation

Write, optimize, and debug **complex SQL queries** (including subqueries, CTEs, window functions, and advanced joins) to extract data from various enterprise data warehouses.

Ensure data integrity and consistency between source systems and front-end reports.

Required Technical Skills & Qualifications

Must-Haves (Non-Negotiable)

Power BI & DAX: Minimum of 1–3 years of hands-on experience building Power BI solutions. Deep understanding of evaluation contexts (Filter Context vs. Row Context) and complex DAX functions (CALCULATE, FILTER, ALL, EARLIER, and time-intelligence).

Expert SQL Skills: High proficiency in writing complex, optimized SQL queries. Must be comfortable handling large datasets, writing Common Table Expressions (CTEs), utilizing window functions (RANK, LEAD/LAG, PARTITION BY), and analyzing query performance.

Nice-to-Haves

Data Modeling: Strong understanding of relational database concepts, star/snowflake schemas, and data normalization/denormalization.

Familiarity with cloud data platforms like Snowflake, Azure Synapse, or AWS Redshift.

Basic knowledge of Python for data manipulation.

Soft Skills & Core Competencies

Problem-Solving: A natural curiosity to dig into data anomalies and find the root cause of discrepancies.

Communication: Ability to explain complex technical data constraints to non-technical business users clearly.

Attention to Detail: Precision in data validation to ensure business decisions are made on 100% accurate reporting.

Job Title: 

Role Overview

We are seeking a high-performing Senior Power BI Analyst / Developer to design, build, and optimize our next-generation enterprise analytics platform. In this role, you will be the core architect of our data visualization layer, turning complex data streams into actionable executive insights.

The ideal candidate bridges the gap between traditional business intelligence and modern cloud data engineering. You will be responsible for building highly performant, scalable data models on Azure and creating seamless, near real-time reporting solutions utilizing streaming data and Change Data Capture (CDC) pipelines.

Key Responsibilities

1. Advanced Data Modeling & Performance Tuning

Design, implement, and maintain enterprise-grade, highly scalable data models (Star and Snowflake schemas) within Power BI and Azure Analysis Services/Fabric.

Optimize complex DAX queries, M code, and data loading processes to ensure sub-second dashboard responsiveness on large-scale datasets.

Implement advanced performance-tuning strategies, including aggregations, incremental refreshing, and hybrid tables (combining Import and DirectQuery).

2. Streaming Analytics & Real-Time Reporting

Architect and implement near real-time dashboards utilizing Power BI streaming datasets, push datasets, and automatic page refreshes.

Integrate reporting front-ends with modern Change Data Capture (CDC) architectures to surface low-latency transactional updates.

Partner with data engineering teams to ingest and model fast-moving event data coming from cloud streams into analytical reporting structures.

3. Azure Architecture Integration

Leverage the Azure data stack (Azure Synapse Analytics, Azure Data Lake Storage Gen2, Azure Databricks, and Azure SQL DB) to build robust downstream reporting layers.

Participate in the orchestration of data pipelines (using Azure Data Factory or Synapse pipelines) to align with dashboard refresh schedules.

Embrace modern Power BI developer workflows using Tabular Editor, ALM Toolkit, DAX Studio, and Git integration for version control.

Required Technical Skills & Qualifications

Experience: 3-5+ years of dedicated experience developing, modeling, and tuning enterprise Power BI solutions, with at least 2+ years operating within an Azure cloud ecosystem.

Expert DAX & Power Query: Mastery of complex DAX computations (time-intelligence, advanced filtering) and optimized M-code data transformations.

Modern Data Paradigms: Proven experience handling Streaming Data or CDC (Change Data Capture) patterns (e.g., streaming from Debezium, Azure Event Hubs, Kafka, or Synapse Link) into Power BI.

Azure Stack Expertise: Solid working knowledge of Azure data resources (Synapse, Data Factory, Azure SQL, or Azure Fabric Lakehouses).

Data Engineering Concepts: Deep understanding of data warehousing concepts, including slowly changing dimensions (SCDs), partitioning, and indexing strategies.

Education: Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or a related quantitative field (or equivalent practical experience).

Preferred (Nice-to-Have) Skills

Microsoft Certifications: PL-300 (Power BI Data Analyst) or DP-600 (Fabric Analytics Engineer).

Experience with CI/CD deployment pipelines for Power BI deployment pipelines or Azure DevOps.

Familiarity with SQL performance tuning (analyzing execution plans, optimizing views, and indexing).

About the job

Must-Have Skills

• 5+ years in data architecture / data engineering, with at least 2+ years in an architect or lead capacity
• Strong SQL — advanced query optimization, indexing, partitioning strategies
• Data modeling — dimensional modeling (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 — 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

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

Position: Data Engineer

Location: Ahmedabad, Gujarat (Onsite)

Employment Type: Full-Time

Experience Required: 3–6 Years

Notice Period: 30–60 Days

Interview Process

• 3 Virtual Interview Rounds
• Final Round at Ahmedabad Office

Required Skills

• Strong experience in Python and SQL
• Hands-on experience with ETL/ELT processes and data pipelines
• Experience with cloud platforms such as AWS, GCP, or Azure
• Knowledge of data warehousing concepts (Snowflake, BigQuery, Redshift, etc.)
• Familiarity with tools such as dbt, Airflow, or similar
• Good communication skills in English
• Strong analytical and problem-solving abilities with a collaborative mindset

Good to Have

• Experience in data modeling and performance optimization
• Exposure to real-time data processing and streaming technologies
• Understanding of data governance and data quality best practices

If this opportunity aligns with your experience and career goals, please share your updated resume

Only Immediate joiners are considered.

Key Skills:

Technical Skills

• Power BI Development: 4-5 years of hands-on experience developing Power BI reports, dashboards, and data models
• DAX: Strong proficiency in DAX (Data Analysis Expressions) for creating measures, calculated columns, and complex calculations
• Power Query / M Language: Expertise in data transformation and ETL processes using Power Query
• Data Modeling: Solid understanding of dimensional modeling, star schema, and data warehouse concepts
• SQL: Proficient in SQL for data extraction, manipulation, and querying relational databases
• Power BI Service: Experience with Power BI Service administration, workspace management, scheduled refreshes, and deployment pipelines
• Custom Visualizations: Experience creating and configuring custom visuals, including use of AppSource visuals and custom visual development using Power BI Visuals SDK
• API Integration: Hands-on experience with Power BI REST APIs for automating deployments, managing workspaces, and embedding reports
• Knowledge of data visualization best practices and UI/UX principles for dashboard design
• Experience with data source connectivity (SQL Server, Azure SQL, Oracle, SAP, Excel, APIs, web services)

Additional Required Qualifications

• Bachelor’s degree in computer science, Information Systems, Business Analytics, or related field
• Strong analytical and problem-solving abilities
• Excellent communication skills to work with both technical and non-technical stakeholders
• Ability to manage multiple projects and prioritize tasks effectively
• Detail-oriented with commitment to delivering high-quality work
• Client-facing experience with ability to gather requirements and present solutions

Preferred Qualifications

• Microsoft Power BI certification (PL-300 or equivalent)
• Experience with Azure ecosystem (Azure Data Factory, Azure Synapse Analytics, Azure SQL Database)
• Knowledge of other Microsoft BI tools (SSRS, SSAS, Excel Power Pivot)
• Familiarity with Python or R for advanced analytics integration
• Experience with Dataflows and incremental refresh strategies
• Understanding of API development for custom visuals or Power BI embedded solutions
• Experience working in Agile/Scrum development environments

Job Title : Alteryx Developer

Experience : 5+ Years (Senior profiles with 8 to 12+ years preferred)

Location : Remote

Employment Type : Contract

Job Summary :

We are looking for an experienced Alteryx Developer to design, develop, and optimize enterprise ETL workflows, data transformation processes, and workflow automation solutions. The ideal candidate should have strong expertise in Alteryx Designer, Alteryx Server, SQL, ETL development, and enterprise data integration.

Mandatory Skills :

Alteryx Designer, Alteryx Server, ETL Development, Workflow Automation, SQL, Data Transformation, Data Migration, SQL Server/Oracle/MySQL/PostgreSQL, API Integration, Azure/AWS, Git.

Key Responsibilities :

• Design and develop scalable Alteryx workflows and ETL pipelines.
• Build reusable macros, analytic apps, and automated workflows.
• Perform data extraction, transformation, validation, and migration.
• Integrate data from databases, APIs, cloud platforms, and flat files.
• Manage Alteryx Server, workflow scheduling, deployment, and production support.
• Optimize workflow performance and ensure data quality.
• Collaborate with business and analytics teams to deliver data solutions.

Required Skills :

• Hands-on experience with Alteryx Designer and Alteryx Server.
• Strong expertise in ETL development, workflow automation, and data transformation.
• Proficiency in SQL with experience working on large datasets.
• Experience with SQL Server, Oracle, MySQL, or PostgreSQL.
• Knowledge of data migration, data validation, and data cleansing.
• Experience integrating data through APIs, cloud platforms, and flat files.
• Hands-on experience with Git and version control.
• Exposure to Azure or AWS cloud platforms.
• Strong analytical, troubleshooting, and performance optimization skills.

Good to Have :

• Python or Java
• Power BI, Tableau, or Spotfire
• Data Warehousing & Data Modeling
• CI/CD, Agile/Scrum
• Alteryx Designer Core Certification

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

About the Role

We are looking for a skilled SQL Server DBA with 4–5 years of experience in SQL Server database administration, performance tuning, and enterprise data integration. The ideal candidate should have hands-on experience working with Product Lifecycle Management (PLM) systems, ERP integrations, and data bridge solutions to enable seamless data exchange between enterprise applications.

Key Responsibilities

· Administer, monitor, and maintain Microsoft SQL Server databases to ensure high availability, security, and performance.

· Design, implement, and support PLM–ERP data bridge solutions for seamless integration between Product Lifecycle Management and ERP systems.

· Develop and optimize SQL queries, stored procedures, views, triggers, and database objects.

· Monitor database performance and perform query optimization, indexing, and troubleshooting.

· Design and implement database backup, recovery, disaster recovery, and high availability strategies.

· Build and maintain ETL processes and data synchronization workflows between PLM, ERP, and other enterprise applications. 

· Collaborate with application development teams to support database design and application deployments.

· Perform database migrations, upgrades, patching, and environment maintenance.

· Ensure database security, user management, and compliance with organizational standards.

· Create and maintain technical documentation, database architecture, and operational procedures. 

Required Skills & Experience

· 4–5 years of hands-on experience as a SQL Server DBA.

· Strong expertise in Microsoft SQL Server (2016/2019/2022 or later).

· Excellent knowledge of SQL, T-SQL, Stored Procedures, Functions, Triggers, Views, and Performance Tuning.

· Experience in database backup, restore, replication, indexing, and high availability (Always On, Log Shipping, Replication).

· Hands-on experience working with Product Lifecycle Management (PLM) systems.

· Experience implementing or supporting PLM–ERP data bridge/integration solutions.

· Knowledge of ERP systems such as SAP, Oracle E-Business Suite, Microsoft Dynamics, Infor, or similar platforms.

· Experience with ETL tools and enterprise data integration.

· Strong troubleshooting and root cause analysis skills. 

Preferred Skills

· Experience with Teamcenter, Windchill, Enovia, Arena PLM, or similar PLM platforms.

· Knowledge of SSIS, SSRS, and SSAS.

· Experience with PowerShell or Python scripting for database automation.

· Exposure to Azure SQL Database or cloud-based SQL environments. 

· Understanding of manufacturing, engineering, or product development processes.

· Familiarity with CI/CD and DevOps practices. 

Educational Qualification

· Bachelor's degree in Computer Science, Information Technology, Engineering, or a related discipline.

Key Competencies

· Strong analytical and problem-solving skills.

· Excellent communication and stakeholder management abilities.

· Ability to work independently in a remote environment.

· Strong attention to detail and commitment to database reliability and performance.

· Ability to manage multiple priorities in a fast-paced environment.

About Ritually

Ritually is building the definitive process discovery platform for back office work. Our product fuses underutilized system telemetry with computer vision to help large enterprise and scaling mid-market companies deeply understand and reimagine their highest value and most repetitive processes for a world where humans and agents work together. We're based in New York and Denver.

We believe deeply in trust (of our customers and each other), craft, customer obsession, and speed.

You'll be joining an AI-native, fast-moving, and repeat founding team. Ritually's founders previously built and exited a startup (Involvio) to Cisco. The company is funded and working with design partners.

The Role

This is a founding applied-AI role. You'll be building our core data pipeline and intelligence layer with the founding team from 0-1 You'll be tackling our largest technical challenges across technologies.

What You'll Do

• Design and build data pipelines that capture and turn high volumes of system activity into structured, queryable data.
• Turn raw activity streams into processes: sessionize event logs, cluster recurring sequences, and use LLMs to label and summarize what's happening.
• Build the evaluation backbone from scratch: stand up synthetic data generation pipelines that produce labeled scenarios to measure accuracy and catch regressions.
• Own data quality and privacy.
• Partner closely with the founders and the rest of engineering to ship features end to end.

What We're Looking For

• 1-4 years of experience in data engineering, AI/ML engineering, or backend work with a data focus (some of this can be project or research experience).
• Strong in Python and/or TypeScript, comfortable in SQL, and able to build data pipelines you can trust.
• Hands-on experience working with LLMs structured output, prompting, and wrangling non-determinism while keeping behavior reliable.
• A practical sense for evaluation: you know that "it looks right" isn't the same as "it's measurably right."
• Care about data privacy and handling sensitive information responsibly.
• Comfort with ambiguity and a real appetite to own a hard, open-ended problem.

Nice to Have

• Background in process mining, sequence / event-log analysis, or workflow analytics.
• Deeper PostgreSQL: window functions, partitioning, pg_cron, query performance.
• Embeddings and vector search (pgvector) or semantic retrieval.
• Familiarity with cloud infrastructure.
• Any prior early-stage startup experience.
• Degree in computer science or a related field.

Required Qualifications

• Bachelor’s degree in marketing, Business Analytics, Computer Science, Engineering, Statistics, Information Systems, or a related field.
• 5+ years of experience in marketing analytics, business intelligence, data analysis, or marketing operations.
• 3+ years of hands-on experience supporting B2B marketing organizations with reporting, campaign measurement, and analytics.
• Demonstrated experience owning enterprise marketing reporting ecosystems and KPI governance frameworks.
• Deep expertise in marketing attribution methodologies, email deliverability concepts, campaign tracking, and marketing performance measurement.
• Hands-on experience with marketing automation platforms, including Eloqua, is required.
• Advanced proficiency in SQL and experience working with large, complex datasets.
• Strong programming and data manipulation skills using Python.
• Hands-on experience with multiple BI and visualization platforms, including:
• Domo
• Snowflake
• Power BI
• Tableau
• Proven experience leading or supporting BI platform migrations and change management initiatives.
• Experience building and maintaining data models, ETL processes, and self-service analytics environments.
• Strong understanding of marketing data architecture, data governance, and metadata management.
• Excellent problem-solving skills with a strong attention to detail and commitment to data accuracy.
• Exceptional communication and stakeholder management skills, with the ability to explain technical concepts to non-technical audiences.
• Ability to work independently, prioritize effectively, and manage multiple projects in a fast-paced environment.

Must-Have Qualifications

• 5+ years of experience in marketing analytics, business intelligence, or marketing operations.
• Proven ownership of marketing reporting infrastructure, KPI governance, and executive-level dashboards.
• Strong expertise in email marketing analytics, including deliverability, attribution, campaign tracking, and performance measurement.
• Hands-on experience with Eloqua and marketing automation ecosystems.
• Advanced SQL and Python skills for data extraction, transformation, analysis, and automation.
• Expert-level proficiency with Snowflake and at least two enterprise BI platforms, including Domo, Power BI, and Tableau.
• Demonstrated success leading BI platform migrations and reporting modernization initiatives.
• Experience partnering with marketing, marketing operations, IT, and data engineering teams.
• Strong understanding of data quality frameworks, governance, and reporting best practices.

Nice-to-Have Qualifications

• Experience with Salesforce CRM, Salesforce Marketing Cloud, HubSpot, Marketo, or similar platforms.
• Experience with modern data stack technologies, including dbt, Alteryx, or data orchestration tools.
• Knowledge of account-based marketing (ABM) measurement and customer journey analytics.
• Experience implementing self-service analytics programs.
• Familiarity with Agile methodologies and project management frameworks.
• Experience working within a global B2B technology organization.

Data Engineer – Databricks & AWS (7+ Years)

Location: Baner, Pune

Work Model:5 days from office

Required Skills

• 7+ years of experience in Data Engineering with strong expertise in Databricks, PySpark, Apache Spark, and SQL.
• Hands-on experience building scalable ETL/ELT pipelines and Data Lake/Lakehouse solutions using Delta Lake.
• Experience with AWS services including S3, Glue, Lambda, IAM, EMR, and Redshift.
• Strong knowledge of Apache Airflow, Git, CI/CD, data quality, performance tuning, and production support.
• Experience with Kafka/Spark Streaming and Banking, Financial Services, or Credit Bureau domains is preferred.

Roles & Responsibilities

• Designed and developed scalable data pipelines using Databricks, PySpark, and AWS to process large-scale credit bureau, customer, loan, and repayment datasets.
• Built ETL/ELT workflows and Delta Lake-based data models to support credit risk analytics, regulatory reporting, and customer profiling.
• Developed and orchestrated batch and near real-time data processing pipelines using Airflow, Kafka, and Spark, ensuring data quality and reliability.
• Optimized Spark workloads through performance tuning techniques, improving processing efficiency and reducing execution time.
• Collaborated with business stakeholders, data architects, and risk teams to deliver data solutions while supporting production environments and operational excellence.

NOTE: One technical round is mandatory to be taken F2F from Pune office.

Job Title : Report (Power BI) Engineer / Developer

Experience : 5+ Years

Work Mode : Remote (4 days/month office visit)

Locations : Noida, Hyderabad, Chennai, Pune, Bengaluru

Job Summary :

We are looking for an experienced Report (Power BI) Engineer / Developer to design and develop business-critical reporting solutions. The ideal candidate should have strong expertise in Power BI dashboards and paginated reports, along with hands-on experience in SQL Server, Snowflake, and Databricks.

Mandatory Skills :

Power BI, Power BI Dashboards, Paginated Reports, SQL Server, T-SQL, Snowflake, Databricks, Data Warehousing, ETL, SQL Query Optimization.

Key Skills Required :

• 5+ years of experience in Power BI development.
• Strong hands-on experience with Power BI Dashboards and Paginated Reports.
• Excellent knowledge of SQL Server and T-SQL development.
• Experience with Snowflake and Databricks.
• Understanding of data warehousing concepts, ETL processes, and SQL performance optimization.
• Strong communication and collaboration skills.

Preferred Skills :

• Experience with cloud data modernization projects.
• Knowledge of data security and compliance practices.

Note : Comprehensive background verification, including education, employment, criminal, credit, and drug screening, is mandatory.

Key Skills:

Technical Skills

• Power BI Development: 4-5 years of hands-on experience developing Power BI reports, dashboards, and data models
• DAX: Strong proficiency in DAX (Data Analysis Expressions) for creating measures, calculated columns, and complex calculations
• Power Query / M Language: Expertise in data transformation and ETL processes using Power Query
• Data Modeling: Solid understanding of dimensional modeling, star schema, and data warehouse concepts
• SQL: Proficient in SQL for data extraction, manipulation, and querying relational databases
• Power BI Service: Experience with Power BI Service administration, workspace management, scheduled refreshes, and deployment pipelines
• Custom Visualizations: Experience creating and configuring custom visuals, including use of AppSource visuals and custom visual development using Power BI Visuals SDK
• API Integration: Hands-on experience with Power BI REST APIs for automating deployments, managing workspaces, and embedding reports
• Knowledge of data visualization best practices and UI/UX principles for dashboard design
• Experience with data source connectivity (SQL Server, Azure SQL, Oracle, SAP, Excel, APIs, web services)

Additional Required Qualifications

• Bachelor’s degree in computer science, Information Systems, Business Analytics, or related field
• Strong analytical and problem-solving abilities
• Excellent communication skills to work with both technical and non-technical stakeholders
• Ability to manage multiple projects and prioritize tasks effectively
• Detail-oriented with commitment to delivering high-quality work
• Client-facing experience with ability to gather requirements and present solutions

Preferred Qualifications

• Microsoft Power BI certification (PL-300 or equivalent)
• Experience with Azure ecosystem (Azure Data Factory, Azure Synapse Analytics, Azure SQL Database)
• Knowledge of other Microsoft BI tools (SSRS, SSAS, Excel Power Pivot)
• Familiarity with Python or R for advanced analytics integration
• Experience with Dataflows and incremental refresh strategies
• Understanding of API development for custom visuals or Power BI embedded solutions
• Experience working in Agile/Scrum development environments

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 the Role:

We are looking for a highly skilled Data Engineer with a strong foundation in Power BI, SQL, Python, and Big Data ecosystems to help design, build, and optimize end-to-end data solutions. The ideal candidate is passionate about solving complex data problems, transforming raw data into actionable insights, and contributing to data-driven decision-making across the organization.

Key Responsibilities:

• Data Modelling & Visualization
• Build scalable and high-quality data models in Power BI using best practices.
• Define relationships, hierarchies, and measures to support effective storytelling.
• Ensure dashboards meet standards in accuracy, visualization principles, and timelines.
• Data Transformation & ETL
• Perform advanced data transformation using Power Query (M Language) beyond UI-based steps.
• Design and optimize ETL pipelines using SQL, Python, and Big Data tools.
• Manage and process large-scale datasets from various sources and formats.
• Business Problem Translation
• Collaborate with cross-functional teams to translate complex business problems into scalable, data-centric solutions.
• Decompose business questions into testable hypotheses and identify relevant datasets for validation.
• Performance & Troubleshooting
• Continuously optimize performance of dashboards and pipelines for latency, reliability, and scalability.
• Troubleshoot and resolve issues related to data access, quality, security, and latency, adhering to SLAs.
• Analytical Storytelling
• Apply analytical thinking to design insightful dashboards—prioritizing clarity and usability over aesthetics.
• Develop data narratives that drive business impact.
• Solution Design
• Deliver wireframes, POCs, and final solutions aligned with business requirements and technical feasibility.

Required Skills & Experience:

• Minimum 3+ years of experience as a Data Engineer or in a similar data-focused role.
• Strong expertise in Power BI: data modeling, DAX, Power Query (M Language), and visualization best practices.
• Hands-on with Python and SQL for data analysis, automation, and backend data transformation.
• Deep understanding of data storytelling, visual best practices, and dashboard performance tuning.
• Familiarity with DAX Studio and Tabular Editor.
• Experience in handling high-volume data in production environment.
• Exposure to Big Data technologies such as:
• PySpark (must have)
• Hadoop
• Hive / HDFS
• Spark Streaming (optional but preferred)

Why Join Us?

• Work with a team that's passionate about data innovation.
• Exposure to modern data stack and tools.
• Flat structure and collaborative culture.
• Opportunity to influence data strategy and architecture decisions.

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.

Job Title : Analytics Engineer

Experience : 6+ Years

Location : Gurgaon | Bangalore | Ahmedabad | Chennai

Work Mode : Work From Office

Employment Type : Contract (6 Months)

About the Role :

We are looking for an experienced Analytics Engineer to design, develop, and optimize scalable data models and analytics solutions that drive business decision-making. The ideal candidate should have strong expertise in SQL, dbt, data modeling, data quality, and modern analytics engineering practices.

Mandatory Skills :

SQL, dbt, Data Modeling, Data Warehousing, ETL/ELT, Query Optimization, Data Quality, Git, CI/CD, Analytics Engineering, Data Transformation, Data Governance, Stakeholder Management.

Key Responsibilities :

• Design and maintain scalable data warehouse models, data marts, and analytical datasets.
• Build and optimize data transformation pipelines using SQL and dbt.
• Develop high-performance SQL queries using CTEs, Window Functions, Complex Joins, and Analytical Functions.
• Implement data quality checks, testing frameworks, and governance best practices.
• Manage end-to-end analytics development lifecycle, from requirement gathering to deployment.
• Work with Git, CI/CD pipelines, and version control best practices.
• Collaborate with business and technical stakeholders to deliver reliable analytics solutions.
• Troubleshoot and optimize data pipelines, models, and query performance.

Required Skills :

• 6+ years of experience in Analytics Engineering, Data Engineering, or related roles.
• Strong expertise in SQL and query performance optimization.
• Hands-on experience with dbt (Data Build Tool).
• Strong understanding of data modeling and data warehousing concepts.
• Experience with Git, CI/CD, and software development best practices.
• Knowledge of data quality frameworks, testing, and validation techniques.
• Ability to independently manage design, development, testing, documentation, and deployment.

Preferred Skills :

• Experience with cloud-based data platforms.
• Exposure to orchestration and scheduling tools.
• Understanding of data governance and compliance frameworks.
• Experience in performance tuning and cost optimization.
• Prior mentoring or technical leadership experience.

What We're Looking For :

• Strong analytical and problem-solving skills.
• Excellent communication and stakeholder management abilities.
• Ability to work independently in a fast-paced environment.
• Passion for building scalable, reliable, and high-quality analytics solutions.

Hiring: GCP Data Engineer (FTE)

📍 Location: Bangalore | Chennai | Pune | Gurgaon | Kolkata

💼 Employment Type: Full-Time

Notice Period - Immediate Joiner ( Serving Notice Period )

Work Mode - Hybrid

We are looking for experienced GCP Data Engineers with strong expertise in building scalable cloud data solutions.

Required Skills:

✔ GCP Data Engineering Experience

✔ BigQuery

✔ SQL & Python

✔ PySpark / Apache Spark

✔ Apache Beam / Dataflow

✔ ETL / ELT Pipeline Development

✔ Airflow / Cloud Composer

Responsibilities:

• Design and develop scalable ETL/ELT pipelines on GCP
• Build and optimize BigQuery solutions
• Process large-scale structured/unstructured data using Spark
• Develop automated workflows and cloud-native data pipelines
• Work with GCP services like Dataflow, Dataproc, Cloud Storage, etc.
• 
  

Good to Have:

• Google Cloud Certifications (Professional Data Engineer / Solution Architect)
• Experience with BigTable, Cloud SQL, Spanner, NoSQL databases

🎓 Qualification: Bachelor’s / Master’s in CS, Engineering, or related field

Job description:

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 standard Big Data / Infra-heavy Data Engineering role. We are specifically looking for a SQL Specialist. Your core strength must lie in relational database development, schema design, and writing high-performance database logic. Python will be your primary tool for moving and orchestrating data, but SQL and database architecture are the heart of this role.

Key Responsibilities

• Database Architecture & Schema Design: Design, implement, and maintain robust relational database schemas, ensuring optimal data modeling (OLTP and OLAP/Data Warehousing).
• Advanced Database Programmability: Write, debug, and optimize complex Stored Procedures, Functions, Triggers, and Views to handle core business logic at the database level.
• Performance Tuning & Indexing: Analyze query execution plans, identify bottlenecks, and implement advanced indexing strategies, partitioning, and query refactoring to ensure sub-second response times.
• Python ETL/ELT Pipelines: Develop, schedule, and maintain scalable data ingestion and transformation pipelines using Python to connect disparate data sources.
• Data Integrity & Governance: Establish 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 complex queries (CTEs, Window Functions, Analytical queries) and server-side logic (Stored Procedures/Functions) in platforms like PostgreSQL, SQL Server, Oracle, or MySQL.
• Advanced Database Optimization: Deep understanding of how databases work under the hood—specifically indexing (B-Tree, Hash, Clustered/Non-Clustered), execution plans, statistics, and locking/concurrency mechanisms.
• Python for Data Ingestion (3+ Years): Proficient in writing clean, modular Python scripts for data manipulation, API integration, and ETL processing (using libraries like Pandas, SQLAlchemy, or custom database connectors).
• Data Modeling Methodologies: Practical experience designing Star/Snowflake schemas, Normalization (3NF), and handling Slowly Changing Dimensions (SCD).

Remote & Soft Skills

• Autonomous Execution: Proven ability to manage your own time, architecture tasks, and deliverables without micromanagement.
• Asynchronous Communication: Exceptional written and verbal English communication skills to collaborate seamlessly across time zones.
• Legacy Refactoring Mindset: You enjoy opening up a massive, poorly optimized 500-line stored procedure and refactoring it for 10x performance.

Nice-to-Haves

• Experience migrating legacy on-premise stored procedures to modern cloud data warehouses (Snowflake, BigQuery, Redshift).
• Familiarity with workflow orchestration tools like Apache Airflow or Prefect.
• Experience with dbt (data build tool).

Work Location: Remote

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)

Role : AWS Data Engineer

Location : Anywhere in India - where cognizant office is available 

Contract duration : 12 months contract

Total Experience : 8-10 years

Budget-15LPA

Relevant Experience : 5+years with required skills & data engineering

Client : Cognizant

Job description : 

Python

Spark

Gradle 

AWS Services (ex: S3, Athena, Redshift, Transfer, SNS, SQS, Event Bridge, Lamda, Glue Data Catalog, RDS, EC2, IAM, Flink)

Kubernetes

Argo

Kafka / Kinesis streaming

SQL

ETL Data Pipelines

Data Modelling

Power BI/ Any reporting tools

New Relic / Terraform

Operational support - Batch monitoring, root cause analysis and fix

We are seeking a highly analytical and detail-oriented Senior Data Analyst to lead data management, reporting, automation, and system development initiatives across the organization. The role focuses on transforming raw data into meaningful insights, designing and implementing automated systems, strengthening data structures, and supporting cross-functional teams with accurate, actionable intelligence to drive strategic and operational business decisions.

Key Responsibilities

1. Data Management & Reporting

• Collect, clean, validate, and consolidate data from multiple internal and external sources.
• Prepare and deliver daily, weekly, and monthly MIS reports for management and departments.
• Design, develop, and maintain dashboards to track KPIs, performance metrics, and operational trends.

2. Database & Data Accuracy Management

• Manage and regularly update internal databases, spreadsheets, and reporting systems.
• Ensure data accuracy, consistency, integrity, and confidentiality across all platforms.
• Implement best practices for data validation, version control, and audit checks.

3. Data Analysis & Business Insights

• Analyze large and complex datasets to identify trends, patterns, gaps, and anomalies.
• Translate data findings into clear, actionable insights and recommendations to support strategic and operational decision-making.

4. Reporting Automation, System Recommendation & Implementation

• Identify opportunities to replace manual or semi-manual processes with automated, data-driven systems.
• Design and implement automated reporting frameworks, dashboards, and data pipelines using Excel (Power Query, VBA, Macros), SQL, BI tools, and Python.
• Proactively suggest new automation tools, system enhancements, or integrations to improve efficiency, accuracy, and scalability.
• Lead the end-to-end implementation of approved automation initiatives, including requirement gathering, system design, testing, deployment, and stabilization.
• Continuously monitor and optimize automated systems in line with business growth and evolving data needs.

5. Cross-Functional Coordination & Support

• Collaborate with Sales, HR, Finance, Operations, and other departments to understand reporting and data requirements.
• Provide support for ad-hoc analysis, custom reports, and special data requests.
• Act as a data partner to department heads for decision support and performance tracking.

6. Documentation & Compliance

• Maintain complete and updated documentation for MIS processes, reports, data models, automation logic, and system changes.
• Ensure compliance with company data governance policies and applicable data protection standards.

System Development & Data Structuring Responsibilities

• Study and understand departmental workflows to evaluate how data is generated, processed, and utilized.
• Review existing manual and digital data systems to identify operational gaps, risks, and improvement opportunities.
• Recommend structured data models, reporting formats, and storage solutions aligned with business requirements.
• Coordinate with department heads to define data structures, access levels, and reporting standards.
• Implement new or upgraded data systems (Excel-based models, cloud platforms, ERP integrations) with minimal operational disruption.
• Design structured data formats and role-based access controls to ensure secure and organized data management.
• Train employees on newly implemented systems and provide post-implementation support.
• Monitor system performance, resolve issues, and continuously improve systems based on user feedback and organizational growth.

Qualifications & Experience

• Bachelor’s degree in Commerce, Statistics, Computer Applications, or a related field.
• 3–5 years of experience in data analytics, reporting, or system automation roles.
• Strong analytical thinking, logical reasoning, and problem-solving abilities.
• High attention to detail with excellent organizational and documentation skills.

Technical Skills (Must Have)

• Advanced Excel: Power Query, Power Pivot, VBA basics, Macros, Charts
• Python or R: Data cleaning, analysis, automation (Pandas, NumPy, etc.)
• BI Tools: Power BI or Tableau (DAX, data modeling, dashboard optimization)
• Data Warehousing Concepts: ETL processes, OLAP, Star/Snowflake schema
• Google Apps Script: Automation in Google Sheets, custom functions, triggers, API integrations, workflow optimization

Data Engineer — AI / BI

Artificial Intelligence & Business Intelligence | Data & Analytics

Who We Are:

Since our inception back in 2006, Navitas has grown to be an industry leader in the digital transformation space, and we’ve served as trusted advisors supporting our client base within the commercial, federal, and state and local markets.

What We Do:

At our very core, we’re a group of problem solvers providing our award-winning technology solutions to drive digital acceleration for our customers! With proven solutions, award-winning technologies, and a team of expert problem solvers, Navitas has consistently empowered customers to use technology as a competitive advantage and deliver cutting-edge transformative solutions.

Position Overview

We are seeking a Databricks Engineer to design, build, and operate a Data & AI platform with a strong foundation in the Medallion Architecture (raw/bronze, curated/silver, and mart/gold layers). This platform will orchestrate complex data workflows and scalable ELT pipelines to integrate data from enterprise systems such as PeopleSoft, D2L, and Salesforce, delivering high-quality, governed data for machine learning, AI/BI, and analytics at scale.

You will play a critical role in engineering the infrastructure and workflows that enable seamless data flow across the enterprise, ensure operational excellence, and provide the backbone for strategic decision-making, predictive modeling, and innovation

Responsibilities:

Data & AI Platform Engineering (Databricks-Centric):

• Design, implement, and optimize end-to-end data pipelines on Databricks, following the Medallion Architecture principles.
• Build robust and scalable ETL/ELT pipelines using Apache Spark and Delta Lake to transform raw (bronze) data into trusted curated (silver) and analytics-ready (gold) data layers.
• Operationalize Databricks Workflows for orchestration, dependency management, and pipeline automation.
• Apply schema evolution and data versioning to support agile data development.

Platform Integration & Data Ingestion:

• Connect and ingest data from enterprise systems such as PeopleSoft, D2L, and Salesforce using APIs, JDBC, or other integration frameworks.
• Implement connectors and ingestion frameworks that accommodate structured, semi-structured, and unstructured data.
• Design standardized data ingestion processes with automated error handling, retries, and alerting.

 Data Quality, Monitoring, and Governance:

• Develop data quality checks, validation rules, and anomaly detection mechanisms to ensure data integrity across all layers.
• Integrate monitoring and observability tools (e.g., Databricks metrics, Grafana) to track ETL performance, latency, and failures.
• Implement Unity Catalog or equivalent tools for centralized metadata management, data lineage, and governance policy enforcement.

Security, Privacy, and Compliance:

• Enforce data security best practices including row-level security, encryption at rest/in transit, and fine-grained access control via Unity Catalog.
• Design and implement data masking, tokenization, and anonymization for compliance with privacy regulations (e.g., GDPR, FERPA).
• Work with security teams to audit and certify compliance controls.

   AI/ML-Ready Data Foundation:

• Enable data scientists by delivering high-quality, feature-rich data sets for model training and inference.
• Support AIOps/MLOps lifecycle workflows using MLflow for experiment tracking, model registry, and deployment within Databricks.
• Collaborate with AI/ML teams to create reusable feature stores and training pipelines.

Cloud Data Architecture and Storage:

• Architect and manage data lakes on Azure Data Lake Storage (ADLS) or Amazon S3, and design ingestion pipelines to feed the bronze layer.
• Build data marts and warehousing solutions using platforms like Databricks.
• Optimize data storage and access patterns for performance and cost-efficiency.

 Documentation & Enablement:

• Maintain technical documentation, architecture diagrams, data dictionaries, and runbooks for all pipelines and components.
• Provide training and enablement sessions to internal stakeholders on the Databricks platform, Medallion Architecture, and data governance practices.
• Conduct code reviews and promote reusable patterns and frameworks across teams.

  Reporting and Accountability:

• Submit a weekly schedule of hours worked and progress reports outlining completed tasks, upcoming plans, and blockers.
• Track deliverables against roadmap milestones and communicate risks or dependencies.

Required Qualifications:

• Hands-on experience with Databricks, Delta Lake, and Apache Spark for large-scale data engineering.
• Deep understanding of ELT pipeline development, orchestration, and monitoring in cloud-native environments.
• Experience implementing Medallion Architecture (Bronze/Silver/Gold) and working with data versioning and schema enforcement in enterprise grade environments.
• Strong proficiency in SQL, Python, or Scala for data transformations and workflow logic.
• Proven experience integrating enterprise platforms (e.g., PeopleSoft, Salesforce, D2L) into centralized data platforms.
• Familiarity with data governance, lineage tracking, and metadata management tools.

Preferred Qualifications:

• Prior UMGC or USM experience preferred.
• Experience with Databricks Unity Catalog for metadata management and access control.
• Experience deploying ML models at scale using MLFlow or similar MLOps tools.
• Familiarity with cloud platforms like Azure or AWS, including storage, security, and networking aspects.
• Knowledge of data warehouse design and star/snowflake schema modeling.

Equal Employer/Veterans/Disabled

Navitas Business Consulting is an affirmative action and equal opportunity employer. If reasonable accommodation is needed to participate in the job application or interview process, to perform essential job functions, and/or to receive other benefits and privileges of employment, please contact Navitas Human Resources.

Navitas is an equal opportunity employer. We provide employment and opportunities for advancement, compensation, training, and growth according to individual merit, without regard to race, color, religion, sex (including pregnancy), national origin, sexual orientation, gender identity or expression, marital status, age, genetic information, disability, veteran-status veteran or military status, or any other characteristic protected under applicable Federal, state, or local law. Our goal is for each staff member to have the opportunity to grow to the limits of their abilities and to achieve personal and organizational objectives. We will support positive programs for equal treatment of all staff and full utilization of all qualified employees at all levels within Navitas.

· Strategy & Architecture: Collaborate with stakeholders to define end-to-end migration strategies, including data mapping, transformation, and validation rules.

· Technical Execution: Utilize tools like SQL DB, CSV2TCXML, IPS Upload, and ETL tools to migrate CAD and metadata.

· Customization: Develop custom migration solutions using BMIDE (Business Modeler IDE), ITK (Integration Toolkit), and SOA (Service Oriented Architecture).

· Project Leadership: Break down projects into manageable work packages, leading both onsite and offshore teams.

· Validation & Quality: Perform validation checks to ensure data integrity and accuracy post-migration.

· Integration Support: Manage CAD integrations (NX, Inventor, Creo) and PLM integrations (T4S, T4O, T4EA).

Key Skills:

Technical Skills

• Power BI Development: 4-5 years of hands-on experience developing Power BI reports, dashboards, and data models
• DAX: Strong proficiency in DAX (Data Analysis Expressions) for creating measures, calculated columns, and complex calculations
• Power Query / M Language: Expertise in data transformation and ETL processes using Power Query
• Data Modeling: Solid understanding of dimensional modeling, star schema, and data warehouse concepts
• SQL: Proficient in SQL for data extraction, manipulation, and querying relational databases
• Power BI Service: Experience with Power BI Service administration, workspace management, scheduled refreshes, and deployment pipelines
• Custom Visualizations: Experience creating and configuring custom visuals, including use of AppSource visuals and custom visual development using Power BI Visuals SDK
• API Integration: Hands-on experience with Power BI REST APIs for automating deployments, managing workspaces, and embedding reports
• Knowledge of data visualization best practices and UI/UX principles for dashboard design
• Experience with data source connectivity (SQL Server, Azure SQL, Oracle, SAP, Excel, APIs, web services)

Additional Required Qualifications

• Bachelor’s degree in computer science, Information Systems, Business Analytics, or related field
• Strong analytical and problem-solving abilities
• Excellent communication skills to work with both technical and non-technical stakeholders
• Ability to manage multiple projects and prioritize tasks effectively
• Detail-oriented with commitment to delivering high-quality work
• Client-facing experience with ability to gather requirements and present solutions

Preferred Qualifications

• Microsoft Power BI certification (PL-300 or equivalent)
• Experience with Azure ecosystem (Azure Data Factory, Azure Synapse Analytics, Azure SQL Database)
• Knowledge of other Microsoft BI tools (SSRS, SSAS, Excel Power Pivot)
• Familiarity with Python or R for advanced analytics integration
• Experience with Dataflows and incremental refresh strategies
• Understanding of API development for custom visuals or Power BI embedded solutions
• Experience working in Agile/Scrum development environments

JD - 

We are looking for a strong Data Engineer having hands on experience in building pipelines using Snowflake and DBT.

Key Responsibilities:

• Develop, maintain, and optimize data pipelines using DBT and SQL on Snowflake DB.
• Collaborate with data analysts, QA and business teams to build scalable data models.
• Implement data transformations, testing, and documentation within the DBT framework.
• Work on Snowflake for data warehousing tasks, including data ingestion, query optimization, and performance tuning.
• Use Python (preferred) for automation, scripting, and additional data processing as needed.

Required Skills:

• 6+ years of experience in building data engineering pipelines.
• Strong hands-on expertise with DBT and advanced SQL.
• Experience working with modern columnar/MPP data warehouses, preferably Snowflake.
• Knowledge of Python for data manipulation and workflow automation (preferred).
• Good understanding of data modeling concepts, ETL/ELT processes, and best practice.

Profile - Databricks Developer

Experience- 5+ years

Location- Bangalore (On site)

PF & BGV is Mandatory

Job Description: -

* Design, build, and optimize data pipelines and ETL/ELT workflows using Databricks and

Apache Spark (PySpark).

* Develop scalable, high performance data solutions using Spark distributed processing.

* Lead engineering initiatives focused on automation, performance tuning, and platform

modernization.

* Implement and manage CI/CD pipelines using Git-based workflows and tools such as

GitHub Actions or Jenkins.

* Collaborate with cross-functional teams to translate business needs into technical

solutions.

* Ensure data quality, governance, and security across all processes.

* Troubleshoot and optimize Spark jobs, Databricks clusters, and workflows.

* Participate in code reviews and develop reusable engineering frameworks.

* Should have knowledge of utilizing AI tools to improve productivity and support daily

engineering activities.

* Strong knowledge and hands-on experience in Databricks Genie, including prompt

engineering, workspace usage, and automation.

Required Skills & Experience:

* 5+ years of experience in Data Engineering or related fields.

* Strong hands-on expertise in Databricks (notebooks, Delta Lake, job orchestration).

* Deep knowledge of Apache Spark (PySpark, Spark SQL, optimization techniques).

* Strong proficiency in Python for data processing, automation, and framework

development.

* Strong proficiency in SQL, including complex queries, performance tuning, and analytical

functions.

* Strong knowledge of Databricks Genie and leveraging it for engineering workflows.

* Strong experience with CI/CD and Git-based development workflows.

* Proficiency in data modeling and ETL/ELT pipeline design.

* Experience with automation frameworks and scheduling tools.

* Solid understanding of distributed systems and big data concepts

The AI Data Engineer will be responsible for designing, building, and operating scalable data pipelines and curated data assets that power machine learning, generative AI, and intelligent automation solutions in an SLA-driven managed services environment. This role focuses on data ingestion, transformation, governance, and operational reliability across cloud and hybrid environments enabling use cases such as knowledge retrieval (RAG), conversational AI, predictive analytics, and AI-assisted service management. The ideal candidate combines strong data engineering fundamentals with an understanding of AI workload requirements, including quality, lineage, privacy, and performance. 

Key Responsibilities 

•Design, build, and operate production-grade data pipelines that support AI/ML and generative AI workloads in managed services environments 

•Develop curated, analytics-ready datasets and data products to enable model training, grounding, feature generation, and AI search/retrieval 

•Implement data ingestion patterns for structured and unstructured sources (APIs, databases, files, event streams, documents) 

•Build and maintain transformation workflows with strong testing and validation 

•Enable Retrieval-Augmented Generation (RAG) by preparing document corpora, chunking strategies, metadata enrichment, and vector indexing patterns 

•Integrate data pipelines with application services 

•Support ITSM and enterprise workflow data needs, including ServiceNow data integration, CMDB/incident data quality improvements, and automation enablement 

•Implement observability for data pipelines (monitoring, alerting, SLAs/SLOs) and perform root cause analysis for pipeline failures or data quality incidents 

•Apply data governance and security best practices 

•Collaborate with ML Engineers, DevOps/SRE, and solution architects to operationalize end-to-end AI solutions 

•Contribute to reusable patterns, templates, and standards within the Bell Techlogix AI Center of Excellence 

Required Qualifications 

•Bachelor’s degree in Computer Science, Engineering, Information Systems, or equivalent practical experience 

•5+ years of experience in data engineering, analytics engineering, or platform data operations 

•Strong proficiency in SQL and Python; experience with data modeling and dimensional concepts 

•Hands-on experience with Azure data services (e.g., Data Factory, Synapse, Databricks, Storage, Key Vault) or equivalent cloud tooling 

•Experience building reliable pipelines with scheduling, dependency management, and automated testing/validation 

•Experience supporting production data platforms with incident management, troubleshooting, and root cause analysis 

•Understanding of data security, privacy, and governance principles in enterprise environments 

Preferred Qualifications 

•Experience enabling AI/ML workloads: feature engineering, training data preparation, and integration with Azure Machine Learning 

•Experience with unstructured data processing for generative AI 

•Familiarity with vector databases or vector search and RAG patterns 

•Experience with event streaming and messaging 

•Familiarity with ServiceNow data model and integration patterns (Table API, export, CMDB/ITSM reporting) 

•Relevant certifications (Microsoft Azure Data Engineer, Azure AI Engineer, Databricks) 

Profile - Databricks Developer

Experience- 5+ years

Location- Bangalore (On site)

PF & BGV is Mandatory

Job Description: -

* Design, build, and optimize data pipelines and ETL/ELT workflows using Databricks and Apache Spark (PySpark).

* Develop scalable, high performance data solutions using Spark distributed processing.

* Lead engineering initiatives focused on automation, performance tuning, and platform modernization.

* Implement and manage CI/CD pipelines using Git-based workflows and tools such as GitHub Actions or Jenkins.

* Collaborate with cross-functional teams to translate business needs into technical solutions.

* Ensure data quality, governance, and security across all processes.

* Troubleshoot and optimize Spark jobs, Databricks clusters, and workflows.

* Participate in code reviews and develop reusable engineering frameworks.

* Should have knowledge of utilizing AI tools to improve productivity and support daily engineering activities.

* Strong knowledge and hands-on experience in Databricks Genie, including prompt engineering, workspace usage, and automation

. Required Skills & Experience:

* 5+ years of experience in Data Engineering or related fields.

* Strong hands-on expertise in Databricks (notebooks, Delta Lake, job orchestration).

* Deep knowledge of Apache Spark (PySpark, Spark SQL, optimization techniques).

* Strong proficiency in Python for data processing, automation, and framework development.

* Strong proficiency in SQL, including complex queries, performance tuning, and analytical functions.

* Strong knowledge of Databricks Genie and leveraging it for engineering workflows.

* Strong experience with CI/CD and Git-based development workflows. * Proficiency in data modeling and ETL/ELT pipeline design.

* Experience with automation frameworks and scheduling tools.

* Solid understanding of distributed systems and big data concepts

Who are we ?

Searce means ‘a fine sieve’ & indicates ‘to refine, to analyze, to improve’. It signifies our way of working: To improve to the finest degree of excellence, ‘solving for better’ every time. Searcians are passionate improvers & solvers who love to question the status quo.

The primary purpose of all of us, at Searce, is driving intelligent, impactful & futuristic business outcomes using new-age technology. This purpose is driven passionately by HAPPIER people who aim to become better, everyday.

Tech Superpowers

End-to-End Ecosystem Thinker: You build modular, reusable data products across ingestion, transformation (ETL/ELT), and consumption layers. You ensure the entire data lifecycle is governed, scalable, and optimized for high-velocity delivery.

The MDS Architect. You reimagine business with the Modern Data Stack (MDS) to deliver Data Mesh implementations and real value. You treat every dataset as a measurable "Data Product with a clear focus on ROI and time-to-insight.

Distributed Compute & Scale Savant: You craft resilient architectures that survive petabyte scale volume and data skew without "breaking the bank. You prove your designs with cost-performance benchmarks, not just slideware.

Al-Ready Orchestrator: You engineer the bridge between structured data and Unstructured/Vector stores. By mastering pipelines for RAG models and GenAl, you turn raw data into the fuel for intelligent, automated workflows.

The Quality Craftsman (Builder @ Heart): You are an outcome-focused leader who lives in the code. From embedding GDPR/PII privacy-by-design to optimizing SQL, Python, and Spark daily, you ensure integrity is baked into every table

Experience & Relevance

Engineering Depth: 7-10 years of professional experience in end-to-end data product development. You have a portfolio that proves your ability to build complex, high-velocity pipelines for both Batch and Streaming workloads

Cloud-Native Fluency: Deep, hands-on experience designing and deploying scalable data solutions on at least one major cloud platform (AWS, GCP, or Azure). You are comfortable navigating the nuances of EMR, BigQuery, or Synapse at scale.

Al-Native Workflow: You don't just build for Al you build with Al. You must be proficient in using Al coding assistants (e.g.. GitHub Copilot) to accelerate your delivery and have a track record of building the data foundations required for Generative Al.

Architectural Portfolio: Evidence of leading 2-3 large-scale transformations-including platform migrations, data lakehouse builds, or real-time

analytics architectures.

Client-Facing Acumen: You have direct experience in a consultative, client-facing role. You can confidently translate a CEO's business vision into a Lead Engineer's technical specification without losing anything in translation.

The "Solver" Mindset: A track record of solving 'impossible data problems-whether it's fixing massive data skew, optimizing spiraling cloud costs, or architecting 99.9% available data services.