As an Engineering Lead at JPMorganChase within Employee Platforms, you are an integral part of an agile team responsible for the hands-on delivery and architecture of secure, stable, scalable data engineering solutions on AWS and Databricks. As a core technical contributor, you own technical direction, develop critical components, drive production reliability, and deliver trusted technology products in support of the firm’s business objectives.
Job responsibilities
- Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems
- Develops secure and high-quality production code, and reviews and debugs code written by others.
- Architects and delivers data platforms and pipelines across ingestion, transformation, curation, and consumption on AWS and Databricks using Spark and Delta.
- Builds reusable Python and/or Scala libraries, frameworks, pipeline templates, and automation.
- Owns operational excellence, including data quality, monitoring and alerting, SLAs, incident triage and RCA, and performance and cost optimization.
- Drives SDLC standards covering CI/CD, testing strategy, secure coding, resiliency patterns, and design and code reviews.
- Leads and mentors engineers and partners with product, analytics, and security teams to deliver roadmaps and execution.
- Uses enterprise-authorized GenAI developer tools such as GitHub Copilot and Claude Code to accelerate refactoring, test generation, and documentation with strong validation and controls.
- Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
- 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.
- Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
- Formal training or certification on data engineering concepts with architecture and production ownership concepts and 5+ years applied experience
- Hands-on practical experience delivering system design, application development, testing, and operational stability
- Strong Python coding skills, and deep experience with Databricks and Spark.
- 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.
- Proficient in all aspects of the Software Development Life Cycle
- Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
- Strong experience with AWS and modern lakehouse and data platform patterns.
- Proven technical leadership through architecture decisions, mentoring, and cross-team influence.
- AWS certification and/or Databricks certification.
- Experience with streaming architectures, including event-driven ingestion, near-real-time processing, and operational support for monitoring, alerting, and recovery.