Data Engineer III - Databricks, Pyspark, Python, AWS
JPMorgan Chase · Bangalore, India · 5d ago
SeniorOn-siteData & Analytics
J.P. Morgan’s Commercial & Investment Bank is a global leader across banking, markets, securities services and payments. Corporations, governments and institutions throughout the world entrust us with their business in more than 100 countries. The Commercial & Investment Bank provides strategic advice, raises capital, manages risk and extends liquidity in markets around the world.
Be part of a dynamic team where your distinctive skills will contribute to a winning culture and team.
As a Data Engineer III - Databricks, Pyspark, Python, AWS at JPMorgan Chase within the Commercial & Investment Bank, you'll serve as a seasoned member of an agile team to design and deliver trusted data collection, storage, access, and analytics solutions in a secure, stable, and scalable way. You are responsible for developing, testing, and maintaining critical data pipelines and architectures across multiple technical areas within various business functions in support of the firm’s business objectives.
Job responsibilities
As a Data Engineer III - Databricks, Pyspark, Python, AWS at JPMorgan Chase within the Commercial & Investment Bank, you'll serve as a seasoned member of an agile team to design and deliver trusted data collection, storage, access, and analytics solutions in a secure, stable, and scalable way. You are responsible for developing, testing, and maintaining critical data pipelines and architectures across multiple technical areas within various business functions in support of the firm’s business objectives.
Job responsibilities
- Design, develop, and maintain big data pipelines (batch and streaming) using PySpark/Spark and Databricks.
- Lead/own data modelling and solution design for data products, including defining target-state architecture, data flows, and transformation patterns.
- Build scalable ingestion and transformation workflows for high-volume datasets, ensuring reliability, quality, and performance.
- Develop and optimize complex SQL transformations, reconciliation queries, and analytical datasets; perform query tuning for large-scale workloads.
- Apply strong data warehousing concepts (dimensional modeling, SCDs, partitioning strategies, etc.) to build well-structured, analytics-ready data layers and Leverage common AWS services, with strong emphasis on S3 and AWS data processing capabilities, to support scalable storage and processing.
- Perform advanced debugging and troubleshooting across distributed Spark workloads (data skew, shuffle tuning, memory/compute optimization).
- Implement engineering best practices: modular design, efficient coding, code reviews, and CI-friendly development approaches.
- Use GitHub/Bitbucket and standard version control workflows to manage codebase, peer reviews, and releases.
- Partner with cross-functional stakeholders to convert requirements into robust big data solutions.
- Uses enterprise-authorized AI capabilities within the work environment to accelerate data pipeline/design analysis and documentation, validating outputs and handling data according to sensitivity and security requirements.
Applies reuse-first, AI-assisted practices to strengthen SDLC-quality routines for data pipelines (e.g., test generation and control validation), ensuring traceability/auditability and alignment to resiliency and security expectations.
Required qualifications, capabilities, and skills
- Formal training or certification on data engineering concepts and 3+ years applied experience
- Experience in data engineering / big data engineering, with strong hands-on delivery and Expert-level SQL skills (must be extremely strong): complex joins, window functions, CTEs, optimization, and analytical problem solving at scale.
- Strong hands-on coding experience with Python in production environments; demonstrated ability to write efficient, maintainable code.
- Deep expertise in Apache Spark (in depth) and PySpark, including performance tuning and distributed processing fundamentals.
- Strong experience with Databricks for large-scale data processing and pipeline development.
- Strong understanding of data warehousing concepts and best practices; proven capability in data modelling and solution design for scalable, maintainable data platforms/products.
- Experience implementing both batch and streaming data processing solutions.
- Familiarity with AWS S3 and common AWS services used in data platforms and processing
- Excellent debugging, troubleshooting, problem-solving skills and experience with GitHub, Bitbucket, and version control best practices.
- Demonstrated experience using enterprise-authorized AI capabilities within the work environment to support data engineering workflows with strong validation habits and awareness of data sensitivity.
Ability to review and validate AI-assisted outputs (e.g., query suggestions, test ideas, or model change summaries) before use, escalating when uncertain and following data handling requirements.
Preferred qualifications, capabilities, and skills
- Good to have: infrastructure provisioning in AWS using Infrastructure as Code (IaC) (e.g., Terraform, AWS CloudFormation).