Cloud Data Engineer

Data Engineer

Location: Bengaluru, India (Hybrid)

Employment Type: Full-time

Experience: 3-5 years

Role Overview  

What We’re Looking For:

• Bachelor’s degree in Computer Science/Engineering or equivalent experience required.
• Experience designing and shipping cloud services products.
• Experience driving and managing technical and architectural dependencies on AWS Cloud.
• A firm understanding of system architecture, cloud computing, PaaS/SaaS design principles, S3, DynamoDB, RDS mandatory.
• Experience in building or maintaining ETL processes and tools, i.e., AWS Glue or any open-source tool.
• Proven system-level design contribution to a current “Live” (in production / under daily high load) multi-region SaaS or PaaS offering.
• Proven experience with S3, DynamoDB, SQL, and AWS RDS services.
• Proficiency in programming languages such as Python.
• Strong analytical and problem-solving skills.
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Required Skills & Experience

• Experience with Python, SQL, and data visualization/exploration tools.
• Familiarity with the AWS ecosystem, specifically S3, DynamoDB, and RDS.
• Communication skills, especially for explaining technical concepts to nontechnical business leaders.
• Ability to work on a dynamic, research-oriented team that has concurrent projects.
• Experience in AWS cost optimization (Savings Plans, Reserved Instances, Spot Instances) and governance frameworks.
• Experience developing solutions using infrastructure orchestration tools (SSM, automation account, Ansible, etc.).
• Excellent leadership, stakeholder management, and communication skills.

What We Offer

• Work with some of the brightest minds in the emerging EV industry.
• Make a tangible impact in reducing carbon emissions and enabling sustainable energy.
• Freedom to suggest, implement, and innovate on systems, processes, and technologies.
• Daily ownership in a high-growth, challenging environment.
• Flexible work environment with hybrid schedules and virtualization options.
• Competitive pay and benefits including health coverage, innovative PTO program, and performance bonuses.

About Us:

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

Job Summary:

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

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

Key Responsibilities:

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

Must-Have Skills:

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

Good-to-Have Skills:

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

Competencies:

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

Why Join Us?

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

Application Details

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

Urgent Hiring – Senior Data Engineer

We are hiring for a Senior Data Engineer for a reputed product-based company in Pune.

Location: Pune – Magarpatta / Baner

Experience: 7–10 Years

Work Mode: 5 Days WFO

Notice Period: Immediate to 30 Days preferred

What We're Looking For

• 7+ years of hands-on experience in Data Engineering
• 5+ years of experience in Python
• 4+ years of experience in Snowflake
• 5+ years of experience in SQL
• 5+ years of experience with ADF / Fivetran / Matillion or equivalent Data Integration tools
• Hands-on experience with AWS / Azure
• Experience with Airflow or equivalent orchestration tools
• Strong experience in ETL/ELT and Data Pipelines
• Experience with APIs, JSON, XML and Webhooks
• Knowledge of CI/CD, Git and automated testing
• Exposure to dbt or similar transformation tools
• Experience in data pipeline monitoring, troubleshooting and performance optimization

Key Responsibilities

• Design, develop and maintain scalable ETL/ELT data pipelines
• Build batch, real-time and on-demand data processing workflows
• Integrate data from cloud and on-premise sources
• Ensure data quality, reliability, performance and SLA adherence
• Work closely with Data Scientists, Analysts, DevOps and Business teams
• Implement data engineering best practices, CI/CD and automated testing
• Troubleshoot pipeline issues and perform root cause analysis
• Optimize data pipelines and SQL queries for performance and cost efficiency

Why Join?

• Opportunity to work with a reputed product-based organization
• Work on modern data engineering and cloud technologies
• Exposure to large-scale data platforms and business-critical data solutions
• Collaborative and technically strong environment

Interested candidates can share their updated CV for immediate consideration.

Must-Have Skills

• Minimum 3 years of experience in Data Engineering / Analytics Engineering / Fintech Data roles
• Must have worked on SMS Parsing, intelligent platform, converting RAW customer SMS data into structured actionable financial signals and enabling downstream usage of SMS derived variables
• Must have established a continuous learning cycle to expand parser coverage
• Experience in Lending / NBFC / Fintech domain
• Experience working with Bureau, SMS, Device, or Banking data
• Strong Python and SQL (production level)
• Experience handling unstructured data (SMS, logs, JSON, APIs)
• Experience building data pipelines, schedulers, and cron jobs
• Strong database design and data modelling skills
• Ability to work in a startup environment with high ownership
• Familiarity with modern platforms like AWS, Snowflake, Google BigQuery, Redshift

Good to Have

• Experience in STPL, especially less than 25K ticket size
• Experience with streaming (Kafka/Kinesis) and orchestration (Airflow or Step Functions)
• Experience with feature stores and risk analytics datasets
• Knowledge of regex, NLP basics for SMS parsing
• Experience supporting real-time decision engines/underwriting systems

Role Summary

This role will be responsible for owning the end-to-end data-structuring layer across the organisation. The individual will transform large volumes of raw, unstructured, and semi-structured data (such as SMS, device, bureau, and app data) into clean, standardised, and analysis-ready datasets. These structured datasets will directly power risk analytics, fraud detection, marketing insights, collections strategy, and policy decisioning.

Key Objective of the Role

Ensure all raw lending data (SMS, Bureau, Device, AA, App logs) is captured, parsed, structured, and stored in a clean analytics-ready format inside databases (PostgreSQL, DynamoDB, AWS stack) so that the Risk and Data Science team can directly use it for feature creation, policy building, and portfolio monitoring.

Core Responsibilities

1. End-to-End Data Ownership

• Design, build, and maintain end-to-end data pipelines (batch + streaming) using AWS native services (Glue, Lambda, Step Functions, Kinesis, S3, Athena, Redshift, EMR/Spark, etc.): ingestion

→ parsing → structuring → storage

• Work closely with Tech, Product, and Data Science to define what data should be captured
• Maintain data documentation, data dictionaries, and schema governance
• Ensure data quality, consistency, and version control

1. Unstructured Data Processing (Highest Priority)

• Parse raw SMS dumps and categorise into salary, EMI, loan apps, collections, credits, debits, OTP, etc.
• Process device fingerprint, behavioural logs, and vendor data (FinBox, AA, Bureau APIs)
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• Convert JSON, logs, and raw API responses into structured feature tables
• Build regex/keyword-based parsers for financial SMS classification

1. Feature Implementation (From Risk & Data Science Team)

• Implement feature creation logic provided by Risk/Data Science team
• Translate business and policy logic into SQL/Python pipelines
• Create reusable feature layers for underwriting, fraud, collections, and monitoring
• Maintain a feature store for consistent model and policy usage

1. Lending Data Understanding (Domain-Specific Requirement)

• Work with Bureau data
• Structure SMS-derived financial variables (income, stress, EMI signals)
• Work with Account Aggregator and bank transaction datasets
• Understand fintech alternate data used in underwriting and fraud detection

1. Data Pipelines & Automation

• Build and maintain ETL/ELT pipelines using Python & SQL
• Create cron jobs for automated data ingestion and feature refresh
• Automate vendor data pulls (Bureau, SMS SDK, AA, device data)
• Ensure low-latency pipelines for real-time underwriting use cases

1. Database Structuring & Storage Architecture

• Structure clean datasets in PostgreSQL (analytics layer)
• Manage raw data storage in DynamoDB / S3 data lake
• Design normalized and denormalised tables for risk analytics
• Optimise database performance for large-scale query workloads

1. Dashboards & Readable Data Layer

• Create analytics-ready datasets, implement & write Metabase queries and convert into dashboards (Metabase / Power BI)
• Enable self-serve data access for Risk, Business, and Founders
• Support ad-hoc analysis requirements from leadership

1. Cross-Functional Collaboration (Very Important)

• The role requires close collaboration with data science, tech, product, and business teams to ensure reliable data pipelines, well-defined schemas, API integrations, logging architecture and high data quality, enabling faster and more accurate decision-making across lending workflows.

Tech Stack (Current Environment)

• AWS Services
• PostgreSQL (Primary analytics DB)
• DynamoDB (Raw/NoSQL storage)
• Python (Pandas, NumPy, ETL frameworks)
• Advanced SQL
• APIs, JSON, and Log Data Handling

About the Role

You'll be at the forefront of designing and implementing robust data platform solutions that power advanced analytics, AI, and machine learning. Working with modern cloud technologies, you'll build scalable data foundations that enable clients to make smarter, data-driven decisions.

Key Responsibilities

• Build scalable data pipelines using Snowflake, AWS, GCP, and Databricks.
• Design and optimize data models for AI and machine learning workloads.
• Develop reliable data foundations for MLOps, governance, and data lineage.
• Integrate data from multiple sources into modern data platforms.
• Leverage Snowpark ML and Snowflake's native AI capabilities.
• Ensure data platforms are secure, scalable, and high-performing.

What We're Looking For

• 5+ years of hands-on experience with Snowflake.
• Strong proficiency in SQL and Python.
• Experience with AWS, Azure, or GCP.
• Knowledge of cloud storage services such as S3, ADLS, or GCS.
• Strong understanding of Dimensional Modeling and Data Vault.
• Experience with Scala or Java is a plus.

Tech Stack

• Data Warehouse: Snowflake
• Programming: SQL, Python, Scala (Good to Have), Java (Good to Have)
• Cloud: AWS, Azure, GCP
• Storage: S3, ADLS, GCS
• AI/ML: Snowpark ML, MLOps

Perks & Benefits

• Public Speaking & Communication Program
• Mentoring Program with Senior Support Leads
• 360° Progress Reviews
• Weekly Learning Sessions & Guilds
• Paid Certifications
• Hackathons & Innovation Days
• Recognition & Rewards Programs
• Team Socials & Annual Offsites
• Employee Assistance Program (24/7 Wellbeing Support)

The Data People Shaping Tomorrow

Our client helps organizations unlock the power of data through modern cloud, analytics, and AI solutions. We believe in creating an environment where talented technologists can learn, innovate, and make a real impact while building cutting-edge data platforms for global clients. If you're passionate about data engineering and want to work with the latest technologies in AI, cloud, and analytics, we'd love to hear from you.

Job Summary

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

Key Responsibilities

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

Required Skills

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

Preferred Skills

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

Qualifications

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

Job Title : Tech Lead – Data Lake Platform

Number of Positions : 2

Experience : 7+ Years

Role Type : Technical Lead / Data Platform Lead

Domain : Data Engineering / Data Platform / AWS

About the Role :

We are looking for an experienced Tech Lead – Data Lake Platform to lead the design, development, and operations of an enterprise-scale AWS-based Data Lake Platform.

The platform will ingest data from multiple business systems, process it through structured data layers, and serve data for analytics, reporting, APIs, and operational applications.

As a Tech Lead, you will be responsible for setting the technical direction, leading data engineering teams, driving platform reliability and performance, and owning the platform's delivery, governance, and production support end to end.

Core Tech Stack :

AWS | S3 | EMR | Glue | Athena | Redshift | DMS | Lambda | RDS | IAM | Airflow | Spark/PySpark | SQL | Data Modeling | Hasura | GraphQL | DBT | PostgreSQL/Aurora | Kafka

Key Responsibilities :

• Own the overall Data Lake Platform architecture, covering data ingestion, staging, curated layers, consumption, analytics, and API/data serving.
• Lead the design and development of production-grade data pipelines using AWS Glue, EMR/Spark, Airflow, DBT, Athena, and Redshift.
• Design scalable batch and analytics pipelines with a focus on reliability, performance, data quality, and maintainability.
• Own the API/data-serving architecture from consumption data → RDS/PostgreSQL → Hasura GraphQL → Lambda/API Gateway.
• Drive improvements in platform stability, including orchestration failures, cluster sizing, pipeline SLAs, query performance, and production reliability.
• Design and implement appropriate AWS security, access control, IAM, PII handling, and data governance practices.
• Lead technical discussions, architecture decisions, code reviews, and engineering best practices.
• Mentor and guide data engineers while ensuring high-quality and scalable engineering delivery.
• Own production support, incident management, troubleshooting, and root-cause analysis (RCA) for critical data platform issues.
• Develop and maintain runbooks, operational procedures, monitoring, and incident response practices.
• Collaborate with business, product, application, and analytics teams to onboard new datasets and support reporting, API, and data consumption requirements.
• Ensure the platform meets defined availability, performance, security, data quality, and compliance requirements.

Must-Have Skills :

• 7+ years of experience in Data Engineering, Data Platform Engineering, or related areas, including experience in technical leadership or architecture.
• Strong hands-on experience with AWS Data Services, including:
• Amazon S3
• AWS EMR
• AWS Glue
• Amazon Athena
• Amazon Redshift
• AWS DMS
• AWS Lambda
• Amazon RDS
• AWS IAM
• Strong production experience with Apache Airflow.
• Strong hands-on experience with Apache Spark / PySpark.
• Strong SQL skills and experience with data modeling, including layered data architecture, data marts, and enterprise data models.
• Experience with Hasura or a similar GraphQL layer for PostgreSQL-based data/API serving.
• Strong understanding of data lake architecture and enterprise data platforms.
• Proven experience leading engineers and driving technical decisions.
• Hands-on experience with production support, troubleshooting, incident management, and RCA.
• Strong understanding of data platform performance, scalability, reliability, and SLA management.

Good-to-Have Skills :

• Experience with DBT and modern data transformation practices.
• Experience with lakehouse table formats on Amazon S3, such as Apache Iceberg or similar technologies.
• Strong knowledge of Amazon Redshift workload optimization, including :
• Distribution keys
• Sort keys
• Spectrum
• External tables
• Experience with Kafka or other streaming/data ingestion technologies.
• Experience with PostgreSQL / Amazon Aurora as a data-serving layer.
• Experience with Lambda and API Gateway for API-based data serving.
• Experience with enterprise data governance, data quality, security, and compliance.
• Experience in BFSI / Banking / Financial Services / Insurance domain.

Ideal Candidate :

The ideal candidate is a hands-on Data Platform / Data Engineering Lead who can operate at both the architecture and implementation level. You should be comfortable designing an AWS Data Lake from end to end, leading engineers, troubleshooting production issues, and working closely with business and application teams to deliver reliable data products.

About Us

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

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

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

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

Our Guiding Principles 

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

Relentless Pursuit of Quality with Pragmatism 

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

Extreme Ownership 

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

Proactive Collaboration 

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

Active Pursuit of Mastery 

  We continuously improve our craft and raise our bar. 

Invite, Give, and Act on Feedback 

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

Ensuring Client Success 

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

Job Description

This is a remote position.

Experience Level

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

Role Overview

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

What You'll Do

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

Requirements

What We're Looking For

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

Benefits

Life at Incubyte  

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

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

Perks

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

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

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

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

Job Overview 

Principal Data Engineer

About Engineering at Mitratech Legal Solutions 

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

About the Role 

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

What You Will Do

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

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

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

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

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

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

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

What We Are Looking For 

To be successful in this role, you will need: 

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

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

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

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

•  Working knowledge of modern cloud platforms such as AWS 

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

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

Nice to Have 

•  Working knowledge of Ruby on Rails and React 

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

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

The Stack Context 

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

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

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

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

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

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

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

Why This Role 

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

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

Description

We are looking for Senior Data Engineers to join our Data Platform team and build scalable, high-performance data platforms that power data processing, analytics, and downstream applications.

The ideal candidate will have strong experience in distributed data processing, ETL pipelines, and Big Data technologies, with hands-on expertise in Apache Spark and Python Scala.

You will be responsible for designing, developing, and optimizing large-scale data pipelines while collaborating closely with cross-functional engineering teams to build reliable, production-grade data solutions.

Key Responsibilities

• Design, develop, and maintain scalable ETL and data processing pipelines for large-scale datasets.
• Build and optimize distributed data applications using Apache Spark and Python Scala.
• Develop reliable, high-performance data pipelines for batch and streaming workloads.
• Design and manage data workflows using Apache Airflow.
• Build and operate data workloads on AWS, with strong usage of Amazon S3 for large-scale data storage.
• Work with large datasets to ensure data quality, consistency, reliability, and performance.
• Collaborate with engineering, product, analytics, and other platform teams to deliver robust data solutions.
• Optimize data workflows for scalability, reliability, performance, and cost efficiency.
• Troubleshoot production issues, identify bottlenecks, and continuously improve platform performance.

Requirements

Candidates who demonstrate:

• 5+ years of experience in Data Engineering, Big Data Engineering, or a similar role.
• Strong hands-on experience with Apache Spark and Scala.
• Experience designing, building, and maintaining large-scale ETL pipelines.
• Strong hands-on experience with AWS, particularly Amazon S3.
• Hands-on experience with Apache Airflow for workflow orchestration and scheduling.
• Strong SQL skills and a solid understanding of distributed data processing concepts.
• Experience working with batch and/or streaming data pipelines.
• Excellent debugging, problem-solving, and performance optimization skills.
• Strong communication and collaboration skills.

Good to Have

• Experience with Databricks and the broader Databricks data platform.
• Familiarity with streaming technologies such as Apache Kafka.
• Experience working on large-scale data platforms handling high-volume data workloads.
• Exposure to additional AWS data services and cloud-native data architectures.