We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.
As a Lead Software Engineer - Data Engineer at JPMorgan Chase within the Cloud Financial Management Technology group, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions 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
- 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
- Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture
- Leads communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies
- Adds to team culture of diversity, opportunity, inclusion, and respect
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 5+ years applied experience
- Proficient experience in system design, testing, and operational ownership
- Advanced Python and cloud-native engineering on AWS (e.g., IAM, VPC, KMS, CloudWatch)
- Proven delivery of production ETL/ELT pipelines (batch and/or streaming) on AWS using services such as AWS Glue, Amazon EMR, AWS Lambda, and orchestration via Amazon MWAA (Airflow) and/or AWS Step Functions
- Strong data engineering fundamentals: CDC/incremental processing, backfills, idempotency, late-arriving data handling, and schema evolution
- Data platform experience with AWS analytics and storage services (e.g., Amazon S3, Amazon Redshift, Amazon Athena, AWS Lake Formation/Glue Data Catalog) and streaming/messaging (e.g., Amazon Kinesis, Amazon MSK)
- Data reliability practices: data quality controls, monitoring/alerting, CI/CD for pipelines (e.g., CodePipeline/CodeBuild), performance & cost optimization, and security/governance compliance (e.g., CloudTrail, least-privilege access)
- 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
- Practical experience leveraging Large Language Models (LLMs) to accelerate advanced coding workflows