Senior Director of Software Engineering

JPMorganChase•Jersey City, NJ

About The Position

As a Senior Director of Software Engineering at JPMorganChase within Corporate Technology, you lead the strategy, engineering, and end-to-end delivery of the department’s Databricks-based data lake and consumption platform on AWS. You provide direction to global engineering teams building data pipelines, APIs, reporting products, and self-service analytics capabilities. Partnering with business, product, technology, and control stakeholders, you translate business priorities into a platform roadmap and deliver secure, governed, scalable capabilities that support production use. Your leadership connects platform architecture, engineering execution, and stakeholder adoption. The role also sets and scales multi-department strategy for agentic AI-enabled engineering and SDLC/TLM automation using enterprise-authorized tools. It establishes portfolio-level standards and guardrails for AI-orchestrated delivery workflows, release governance, automated test modernization, resilience engineering, and incident response acceleration, with a focus on validation, security, resiliency, traceability, and reuse.

Requirements

Formal training or certification in software engineering concepts and 10+ years of applied experience, including 5+ years leading technologists in the delivery of complex engineering capabilities.
Experience leading large or cross-functional, globally distributed engineering teams, including hiring, developing, and recognizing talent.
Deep technical or architectural expertise in Databricks on AWS, including Unity Catalog, Delta Sharing, Lakehouse Federation, and data pipeline development.
Demonstrated success 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.
Demonstrated ability to influence senior stakeholders in highly matrixed organizations and translate business requirements into platform architecture, delivery priorities, and measurable outcomes.
Experience leading complex initiatives spanning system design, testing, deployment, resilience, and operational stability, supported by extensive practical cloud-native experience.
Experience leading multi-organization adoption of agentic AI-enabled engineering operating models using enterprise-authorized tools, including defining governance (human-in-the-loop decisioning and quality gates), measurement frameworks, and secure handling of sensitive inputs and outputs across teams.
Deep understanding of responsible AI risk, controls, and resiliency/security expectations at scale, with demonstrated ability to advise senior leaders on safe adoption, portfolio governance, and reuse-first strategies.

Nice To Haves

Experience in financial services or another highly regulated industry.
Experience with federated data access and sharing across multiple workspaces, catalogs, business units, or legal entities.
Familiarity with audit and control-testing programs and the implications of coarse-grained and fine-grained data access models.

Responsibilities

Own the platform strategy, roadmap, and delivery across data onboarding, access provisioning, and consumption capabilities, balancing business demand, engineering dependencies, and operational risk.
Lead and develop global, distributed engineering teams delivering data pipelines, APIs, front-end data products, and self-service analytics; establish technical standards, delivery accountability, and cross-team collaboration.
Direct data onboarding and integration using Unity Catalog, Delta Sharing, and Lakehouse Federation, enabling governed access across workspaces, catalogs, and lines of business.
Partner with senior business, product, technology, audit, and compliance stakeholders to prioritize investments, resolve competing objectives, and communicate architectural choices and delivery trade-offs.
Establish and enforce onboarding controls for data classification, metadata, lineage, and access, ensuring source-data classification approval and clear ownership before data is made available for consumption.
Own platform production readiness, scalability, resilience, and audit readiness; drive remediation of control gaps involving service identities, credential lifecycle, classification coverage, and schema changes.
Define and monitor service-level objectives for stakeholder-facing data products, with clear accountability for reliability, support, and operational performance.
Evaluate and adopt Databricks-native capabilities, including declarative pipelines and conversational analytics, where they improve usability, governance, delivery efficiency, or cost.
Set standards for enterprise-authorized AI-assisted and agentic engineering workflows across teams, including automated testing and release governance, with appropriate human oversight, security, validation, resilience, and traceability.
Set and scale multi-department strategy for agentic AI-enabled engineering and SDLC/TLM automation to drive firmwide objectives, including portfolio-level standards for AI-orchestrated delivery workflows, release governance, automated test modernization, resilience engineering, and incident response acceleration.
Establish guardrails for validation, security, resiliency, traceability, and reuse in AI-enabled engineering and automation.
Apply knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to drive cross-domain reuse and measurable capacity unlock outcomes across departments.