Lead Software Engineer-Java, Spring Boot

JPMorgan Chase & Co.; Bengaluru; On-site; INR 4,000,000 - 6,000,000; Full time

Job summary

JPMorgan Chase & Co. in Bengaluru invites a Lead Software Engineer to design and deliver scalable data pipelines, backend services, and infra-as-code for large-scale analytics.

The role focuses on PySpark/AWS Glue ETLs, Java/Python microservices, and modern CI/CD with Terraform/Spinnaker. You will mentor engineers and drive secure, efficient development practices in a financial data domain.

Qualifications

• Bachelor's degree in CS/Engineering or equivalent experience.
• At least 5+ years in professional software engineering.
• Proficient in Python or Java and strong design fundamentals.
• Hands-on AWS experience and data pipeline work with SQL databases.
• Experience with Spark and data lake/table formats is a plus.
• Proven CI/CD and Git-based workflow expertise; strong testing discipline.
• Experience leading use of approved AI-assisted development tools.

Responsibilities

• Design, build, and maintain production-grade ETL/data pipelines (PySpark, AWS Glue).
• Develop backend microservices and APIs (Java/Spring Boot, Python).
• Write clean, tested code with unit/integration tests.
• Implement infrastructure-as-code and CI/CD pipelines (Jenkins, Spinnaker, Terraform).
• Optimize data workflows for cost and performance; collaborate with teams.
• Review design/code, troubleshoot, and mentor junior engineers.
• Leverage AI coding assist tools with peer reviews and secure coding standards.

Skills

Python

Java

AWS

Data pipelines

Spark

CI/CD

Testing

AI-assisted tooling

Git workflows

Education

Bachelor's degree in CS/Engineering

Tools

Apache Iceberg

Airflow

Jenkins

Spinnaker

Terraform

CloudFormation

Docker

Job description

We have an exciting and rewarding opportunity for you to take your software engineering career to the next level.

As a Lead Software Engineer at JPMorgan Chase within the Data Platform team, you'll design and deliver scalable data pipelines, backend services, and infrastructure-as-code that support large-scale revenue and analytics processing.

Job responsibilities

• Design, build, and maintain production-grade ETL/data pipelines using PySpark, AWS Glue, and Apache Iceberg.
• Develop and operate backend microservices and APIs (Java/Spring Boot, Python).
• Write clean, well-tested, maintainable code with strong unit and integration test coverage.
• Build and manage infrastructure-as-code and CI/CD pipelines (Jenkins, Spinnaker, Terraform/CloudFormation).
• Optimize data workflows for performance, cost, and reliability.
  Collaborate with data engineers, analysts, and product stakeholders to translate requirements into robust solutions.
• Participate in design and code reviews; troubleshoot production issues and drive root-cause fixes.
  Mentor junior engineers and contribute to engineering best practices.
• Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness.
• Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation

Required qualifications, capabilities, and skills

• Bachelor's degree in Computer Science, Engineering, or related field (or equivalent experience).
• 5+ years of professional software engineering experience.
• Strong proficiency in at least one of Python or Java, plus solid software design fundamentals.
• Hands-on experience with AWS cloud services (S3, Glue, Lambda, IAM, RDS/Aurora).
• Experience building data pipelines and working with SQL and relational databases (e.g., PostgreSQL).
• Familiarity with distributed data processing (Spark) and data lake/table formats.
• Proficiency with Git-based workflows and CI/CD practices.
• Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
• Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
• Strong testing discipline (unit, integration) and debugging skills.
• Background in financial, revenue, or large-scale analytics data domains.

Preferred Qualifications

• Experience with Apache Iceberg or similar transactional table formats.
• Experience with workflow orchestration (Airflow) and containerization (Docker).
• Experience with infrastructure-as-code and pipeline tooling (Jenkins, Spinnaker, Terraform).