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About JPMorgan Chase
JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.
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The Senior Director of Software Engineering leads the strategy, engineering, and end-to-end delivery of the Databricks-based data lake and consumption platform on AWS within Corporate Technology. This role involves directing global engineering teams to build data pipelines, APIs, reporting products, and self-service analytics capabilities. Key responsibilities include owning the platform roadmap, establishing technical standards, and managing data onboarding using Unity Catalog, Delta Sharing, and Lakehouse Federation. The position requires translating business priorities into secure, governed, and scalable capabilities while ensuring production readiness and audit compliance. The candidate must possess deep architectural expertise in Databricks on AWS, cloud-native system design, and agentic AI-enabled engineering workflows. This role solves complex problems regarding federated data access, data classification, lineage, and operational stability in a highly regulated environment.
Own the platform strategy, roadmap, and delivery for the Databricks-based data lake and consumption platform on AWS.
Lead global engineering teams in building data pipelines, APIs, front-end data products, and self-service analytics.
Direct data onboarding and integration using Unity Catalog, Delta Sharing, and Lakehouse Federation to enable governed access.
Establish and enforce controls for data classification, metadata, lineage, and access across all workspaces and catalogs.
Manage platform production readiness, scalability, resilience, and audit readiness while remediating control gaps.
Define and monitor service-level objectives for stakeholder-facing data products to ensure reliability and operational performance.
Set standards for enterprise-authorized AI-assisted engineering workflows, including automated testing and release governance.
Translate business priorities into a secure, governed, and scalable platform architecture for production use.
What we're looking for
Formal training or certification in software engineering concepts and 10+ years of applied experience.
5+ years of experience leading technologists in the delivery of complex engineering capabilities.
Experience leading large or cross-functional, globally distributed engineering teams, including hiring and developing talent.
Deep technical or architectural expertise in Databricks on AWS, including Unity Catalog, Delta Sharing, and Lakehouse Federation.
Experience delivering enterprise-scale data platforms with production-grade access controls, lineage, data classification, and operational support.
Experience delivering data consumption and self-service analytics capabilities spanning pipelines, APIs, and reporting or front-end data products.
Experience leading multi-organization adoption of agentic AI-enabled engineering operating models and defining associated governance and measurement frameworks.
Deep understanding of responsible AI risk, controls, and resiliency/security expectations at scale.
Experience in financial services or another highly regulated industry (preferred).
Experience with federated data access and sharing across multiple workspaces, catalogs, business units, or legal entities (preferred).
Familiarity with audit and control-testing programs and the implications of coarse-grained and fine-grained data access models (preferred).
AI
Data Engineering
Python
Java
Scala
SQL
Distributed Systems
ETL
ELT
SDLC
Data Quality
Observability
Resilience Engineering
Security
Monitoring
Alerting