at J.P. Morgan
Bulge Bracket Investment BanksPosted 6 days ago
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**Lead Software Engineer - Data Engineer** drives agile team's data engineering efforts at JPMorgan Chase. This role requires 5+ years of software engineering experience and advanced proficiency in Python, AWS cloud, and ETL/ELT pipelines. Key responsibilities include designing, developing, and troubleshooting innovative software solutions, leading AI-assisted engineering practices adoption, and ensuring code quality, delivery speed, and operational excellence. Essential skills encompass data engineering fundamentals, data platform experience with AWS analytics services, and data reliability practices. The role demands excellent system design, testing, and operational ownership skills, as well as the ability to lead communities of practice and mentor teams. Experience with AI-assisted development tools and Large Language Models is beneficial.
- Compensation
- Not specified
- City
- Atlanta
- Country
- United States
Currency: Not specified
Full Job Description
Location: Atlanta, GA, United States
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 firms 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
