JPMorgan Chase & Co.

Software Engineer III - Bigdata, Spark, AWS - (Data Engineering)

JPMorgan Chase & Co. · Bengaluru, Karnataka, India

Financial Services · 10,001+ employees

4 h ago Closes in 1d
Mid (2-5 yrs) Full-time India
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About the role

Design and deliver secure, scalable technology products as part of an agile team within Corporate Technology. Responsibilities include developing high-quality production code, creating architecture artifacts, and leveraging AI tools to improve delivery speed.

What they look for

Java AWS Python Spark PySpark System Design Agile CI/CD Database Technologies AI-assisted Development SDLC Big Data

Requirements

Requires 3+ years of experience in software engineering fundamentals and expert proficiency in Java, AWS, Python, and Spark. Candidates must have strong end-to-end SDLC experience and a solid understanding of Agile engineering practices.

Full description

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

As a Software Engineer III at JPMorganChase within the Corporate Technology, you serve as a seasoned member of an agile team to design and deliver trusted market-leading technology products in a secure, stable, and scalable way. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.

Job responsibilities

  • Executes software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
  • Creates secure and high-quality production code and maintains algorithms that run synchronously with appropriate systems
  • Produces architecture and design artifacts for complex applications while being accountable for ensuring design constraints are met by software code development
  • Gathers, analyzes, synthesizes, and develops visualizations and reporting from large, diverse data sets in service of continuous improvement of software applications and systems
  • Proactively identifies hidden problems and patterns in data and uses these insights to drive improvements to coding hygiene and system architecture
  • Contributes to software engineering communities of practice and events that explore new and emerging technologies
  • 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

  • Formal training or certification in software engineering fundamentals, with 3+ years of practical, applied experience.
  • Strong end-to-end experience across the full SDLC, delivering work within an Agile framework.
  • Expert proficiency in Java, AWS, Python, Spark/PySpark, and database technologies.
  • Hands-on capability in system design, application development, testing, and maintaining production/operational stability.
  • Proficient programming skills in one or more modern development languages.
  • Experience building, debugging, and maintaining code in a large enterprise environment, including database querying.
  • Solid understanding of Agile engineering practices including CI/CD, application resiliency, and security.
  • Demonstrated knowledge of software applications and technical processes across domains (e.g., cloud, AI/ML, mobile) with experience using one or more leading cloud providers.
  • Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
  • Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices.

Preferred qualifications, capabilities, and skills

  • Knowledge of industry-wide Big Data technology trends and best practices.
  • Good to Have Databricks Exposure
  • Exposure to cloud technologies