Senior Director of Software Engineering

Your opportunity to make a real impact and shape the future of financial services is waiting for you. Let’s push the boundaries of what’s possible together.

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.

Job 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.
- Sets and scales multi-department strategy for agentic AI-enabled engineering and SDLC/TLM automation (using enterprise-authorized tools within the work environment) to drive firmwide objectives (speed, scalability, reliability, and cost-to-serve), including portfolio-level standards for AI-orchestrated delivery workflows, release governance, automated test modernization, resilience engineering, and incident response acceleration; establishes guardrails for validation, security, resiliency, traceability, and reuse.
- Applies 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.

Required qualifications, capabilities, and skills

- 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 within the work environment), including defining governance (human-in-the-loop decisioning, quality gates), measurement frameworks, and secure handling of sensitive inputs/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.

Preferred qualifications, capabilities, and skills

- 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.