Lead Software Engineer - Python, Databricks and AWS

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 at JPMorgan Chase within the Corporate Technology, 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.

Architect the lake house: design bronze/silver/gold (or equivalent) layers, domain data products Deliver ingestion at scale: implement resilient ingestion from AWS sources into Databricks (batch + streaming), including CDC where needed. Build maintainable pipelines: use Delta Live Tables (DLT) and/or standard Jobs with clear modular structure, testing, and documentation. Operational excellence: productionize workloads via Databricks Workflows/Jobs, robust retries, checkpointing, idempotency, and safe re-runs. Governance by design: enforce least privilege, data classification (PII), auditing, lineage/metadata, and controlled sharing/consumption. 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. Performance & cost management: tune Spark/Delta workloads, right-size clusters, optimize storage layout, and manage job/warehouse spend. Lead and mentor: set engineering standards, run design reviews, drive code quality, and upskill engineers in Spark/Databricks best practices, cross-functional delivery: translate stakeholder needs into technical plans, communicate tradeoffs, and align with security/platform teams. CI/CD and IaC: Terraform (preferred) for Databricks + AWS resources; promotion across environments. Testing: unit/integration tests for transformations, data quality checks, contract testing, and replay/backfill procedures, version control & code review discipline; clear documentation and runbooks. Required qualifications, capabilities, and skills Formal training or certification on software engineering concepts and 5+ years hands on Software Development Life Cycle experience Strong data engineering experience, including proven leading delivery/architecture for multi-team data platforms. Hands-on experience building and operating a Databricks Lakehouse Hosted in AWS Deep experience with Delta Lake (ACID tables, partitioning, schema evolution, Proven experience with Spark on Databricks (performance tuning, cluster sizing, skew mitigation, joins, caching, file sizing). Experience with streaming and batch pipelines (Structured Streaming; incremental processing; backfills; late-arriving data). Strong AWS fundamentals for data platforms: S3, IAM, KMS, networking basics (VPC/security groups), logging/auditing. Experience implementing data governance/security controls in Databricks (e.g., Unity Catalog, table/column permissions, credential passthrough patterns as applicable). 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