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AWS Lead Software Engineer-Python/PySpark
lead · Technology / Software Development
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As an AWS Lead Software Engineer-Python/PySpark at JPMorgan Chase within the Consumer and Community Banking Home Lending team, 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 break down technical problems
- Develops secure high-quality production code, and reviews and debugs code written by others
- 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
- Design reusable data processing and data quality frameworks, writing production-ready Python/PySpark with testing, performance tuning, and maintainable patterns
- Build and continuously improve reliable batch and streaming data pipelines, enhancing scalability, security, and operational excellence for critical data systems
- Develop data models and transformations using SQL and dbt to support analytics, BI, and reporting use cases
- Create and operate workflow orchestration (e.g., Airflow) to schedule, monitor, and troubleshoot data jobs, leveraging infrastructure-as-code (e.g., Terraform) to provision and manage platform infrastructure
- 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.
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 5+ years applied experience
- Hands-on experience delivering end-to-end software solutions across system design, application development, testing, and operational stability; proficient in all aspects of the SDLC
- Advanced programming skills, with strong Python expertise (including unit and integration testing) and advanced PySpark for building and maintaining data processing solutions
- Proficiency with automation, CI/CD, and continuous delivery practices
- Hands-on experience building and operating cloud-native solutions on AWS (e.g., EKS/ECS, Lambda, API Gateway, VPC, IAM, S3, RDS/DynamoDB, SQS/SNS, CloudWatch/CloudTrail)
- Experience building and running cloud data platforms on AWS, Google Cloud, or Azure
- Experience with large-scale distributed data processing, performance tuning, and optimization
- Strong SQL/Spark SQL skills, including data modeling, query optimization, and execution plan analysis
- Experience with modern warehouse/lakehouse ecosystems (e.g., Redshift, BigQuery, Snowflake; Spark/Flink/Trino; Iceberg/Hudi) and using approved AI-assisted development tools with standards to validate correctness, performance, and security
- 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
- Experience in financial services, ideally supporting home lending products and processes
- Familiarity with modern front-end technologies and patterns for building user-facing experiences
- Strong data modeling experience for analytics and reporting use cases
- Knowledge of data platform security, risk, compliance, and governance practices
- Experience building delivery automation for data/platform services, including CI/CD and containerized deployments (Docker, Kubernetes)
- Expertise in modern data/streaming platforms and patterns (Kafka topic design and operations; Spark Structured Streaming and streaming ETL)
- Ability to coach and mentor teammates, contribute to a collaborative and inclusive culture, and use AI-assisted engineering tools in an enterprise-safe way (spec-driven work, refactoring, code review); plus experience with Delta Lake and how it compares to Iceberg