AWS Lead Software Engineer-PythonPySpark

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As an AWS Lead Software Engineer-Python/PySpark at JPMorgan Chase within the Consumer and Community Banking Home Lending team youare 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 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