JPMorgan Chase & Co.

Software Engineer III - Data Engineer - Java, Spark, Databricks

JPMorgan Chase & Co. Bengaluru, Karnataka, India

Financial Services · 10,001+ employees

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

You will design and deliver secure, scalable technology solutions as part of an agile team while troubleshooting complex technical problems. You are also responsible for developing high-quality code and leveraging data insights to drive improvements in system architecture.

What they look for

Java Spark Databricks Spring Spring Boot SQL Data Engineering System Design Cloud Computing AWS GCP Azure Data Modeling Agile AI-assisted coding

Requirements

Candidates must have at least 3 years of applied software engineering experience including proficiency in Java, Spring, and database querying. Experience with cloud platforms and data engineering tools like Spark and Databricks is required.

Full description

We have an exciting and rewarding opportunity for you to take your software engineering career to the next level, while building technology that matters.

Job summary

As a Software Engineer III at JPMorgan Chase within 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

  • Execute software solutions, design, development, and technical troubleshooting, thinking beyond routine or conventional approaches to build solutions or break down technical problems
  • Create secure, high-quality production code and maintain algorithms that run synchronously with appropriate systems
  • Leverage enterprise-authorized, artificial intelligence-assisted coding tools within the work environment to improve code quality, delivery speed, and productivity (for example, code generation and refactoring, unit test creation, and documentation), while validating outputs through peer review, automated testing, and secure coding standards
  • Apply knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized artificial intelligence-assisted development and automation capabilities, to improve the value realized by automation; contribute learnings and reusable patterns to improve broader team effectiveness
  • Produce architecture and design artifacts for complex applications, while being accountable for ensuring design constraints are met through software code development
  • Gather, analyze, synthesize, and develop visualizations and reporting from large, diverse data sets in service of continuous improvement of software applications and systems
  • Proactively identify hidden problems and patterns in data and use these insights to drive improvements to coding hygiene and system architecture

Required qualifications, capabilities, and skills

  • Formal training or certification on software engineering concepts and at least 3+ years of applied experience
  • Hands-on practical experience in system design, application development, testing, and operational stability
  • Experience developing, debugging, and maintaining code in a large corporate environment using one or more modern programming languages and database querying languages
  • Hands-on experience with advanced Java frameworks (including Spring and Spring Boot) and experience with database design, data modeling, and Structured Query Language querying
  • Experience with at least one cloud vendor: Amazon Web Services, Google Cloud Platform, or Microsoft Azure
  • Experience working as a data engineer transforming large, complex data using enterprise tools
  • 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

  • Familiarity with modern front-end technologies
  • Exposure to cloud technologies
  • Familiarity with Apache Spark, Databricks, and data lake architecture
  • Familiarity with application programming interfaces, microservices frameworks, container technologies, and workflows

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