JP Morgan Chase

Runs a consumer bank and an investment bank

Data Engineer 3 - Multiple Teams

Data EngineeringConfirmed live in the last 24 hours

Full-timeBengaluru, Karnataka, IndiaNo salary listedIn-person

About the Job

Requirements

Formal training or certification in data engineering concepts and at least 3 years of applied experience.
Experience in data engineering or big data engineering, with hands-on delivery and very strong SQL skills, including complex joins, window functions, common table expressions, optimization, and analytical problem-solving at scale.
Strong hands-on production coding experience with Python and the ability to write efficient, maintainable code.
Deep expertise in Apache Spark 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, with demonstrated capability in data modelling and solution design for scalable, maintainable data platforms or products.
Experience implementing both batch and streaming data processing solutions.
Familiarity with AWS S3 and common AWS services used in data platforms and processing.
Experience with debugging, troubleshooting, problem-solving, GitHub, Bitbucket, and version control best practices.
Demonstrated experience using enterprise-authorized AI capabilities in the work environment to support data engineering workflows, with validation habits and awareness of data sensitivity.
Ability to review and validate AI-assisted outputs, such as query suggestions, test ideas, or model change summaries, before use; escalate when uncertain and follow data-handling requirements.

Responsibilities

Design, develop, and maintain big data batch and streaming pipelines using PySpark, Spark, and Databricks.
Lead and 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 data warehousing concepts, including dimensional modelling, slowly changing dimensions, and partitioning strategies, to build well-structured, analytics-ready data layers.
Leverage common AWS services, especially S3 and AWS data processing capabilities, to support scalable storage and processing.
Debug and troubleshoot distributed Spark workloads, including data skew, shuffle tuning, and memory and compute optimization.
Implement engineering best practices, including modular design, efficient coding, code reviews, and CI-friendly development approaches.
Use GitHub or Bitbucket and standard version control workflows to manage the codebase, peer reviews, and releases.
Partner with cross-functional stakeholders to convert requirements into robust big data solutions.
Use enterprise-authorized AI capabilities within the work environment to accelerate data pipeline and design analysis and documentation, validating outputs and handling data according to sensitivity and security requirements.
Apply reuse-first, AI-assisted practices to strengthen SDLC-quality routines for data pipelines, such as test generation and control validation, ensuring traceability, auditability, and alignment with resiliency and security expectations.

Desired Qualifications

Experience provisioning infrastructure in AWS using Infrastructure as Code, such as Terraform or AWS CloudFormation.

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

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).

About the Company

JP Morgan Chase

Runs a consumer bank and an investment bank

JPMorgan Chase runs the largest bank in the United States, which serves households and small businesses as Chase and large companies, governments, and investors as J.P. Morgan. During the 2008 financial crisis it bought the failing investment bank Bear Stearns and lender Washington Mutual, and in 2023 it took over First Republic Bank after regulators seized it. Chase takes deposits and earns interest on credit cards, mortgages, and car loans, while the investment side earns fees for advising on mergers, raising money for companies, trading stocks and bonds, and managing wealth. Formed in a 2000 merger, JPMorgan Chase traces its roots to 1799 and is based in New York City.

Company size: 10,001+
Company stage: IPO
Headquarters: New York City, New York
Founded: 1799

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