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:
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Design, build, and maintain production-grade ETL/data pipelines using PySpark, AWS Glue, and Apache Iceberg.
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Develop and operate backend microservices and APIs (Java/Spring Boot, Python).
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Write clean, well-tested, maintainable code with strong unit and integration test coverage.
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Build and manage infrastructure-as-code and CI/CD pipelines (Jenkins, Spinnaker, Terraform/CloudFormation).
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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
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Bachelor's degree in Computer Science, Engineering, or related field (or equivalent experience).
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5+ years of professional software engineering experience.
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Strong proficiency in at least one of Python or Java, plus solid software design fundamentals.
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Hands-on experience with AWS cloud services (S3, Glue, Lambda, IAM, RDS/Aurora).
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Experience building data pipelines and working with SQL and relational databases (e.g., PostgreSQL).
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Familiarity with distributed data processing (Spark) and data lake/table formats.
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Proficiency with Git-based workflows and CI/CD practices.
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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.
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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
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Strong testing discipline (unit, integration) and debugging skills.
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Background in financial, revenue, or large-scale analytics data domains.
Preferred Qualifications
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Experience with Apache Iceberg or similar transactional table formats.
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Experience with workflow orchestration (Airflow) and containerization (Docker).
Experience with infrastructure-as-code and pipeline tooling (Jenkins, Spinnaker, Terraform).
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