Senior Director of Software Engineering

JPMorgan Chase & Co.•Jersey City, NJ

3d•$232,750 - $325,000

About The Position

As a Senior Director of Software Engineering at JPMorganChase within Data Technology, you lead multiple technical areas, manage the activities of multiple departments, and collaborate across technical domains. Your expertise is applied cross-functionally to drive the adoption and implementation of technical methods within various teams and aid the firm in remaining at the forefront of industry trends, best practices, and technological advances.

Requirements

Formal training or certification on software engineering concepts and 10+ years applied experience.
In addition, 5+ years of experience leading technologists to manage, anticipate and solve complex technical items within your domain of expertise.
Deep expertise in distributed data processing and runtime systems, including: Batch: PySpark and modern Spark ecosystem patterns, Streaming: Flink (or equivalent) and streaming design (state, exactly-once/at-least-once, backpressure), Platform/Tooling: DBX or comparable platform tooling, CI/CD, release management, environment management, Serverless/Managed compute patterns and trade-offs.
Strong architecture capability across compute, storage, networking, security, and observability.
Proven ability to lead cross-functional delivery across multiple organizations with complex stakeholders.
Experience operating in regulated, high-control environments with strong risk and operational discipline.
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.
Experience leading complex projects supporting system design, testing, and operational stability.
Experience with hiring, developing, and recognizing talent.
Extensive practical cloud native experience.

Nice To Haves

Experience building developer platforms and paved roads (templates, libraries, golden paths, self-service portals).
Hands-on familiarity with data governance, lineage/metadata management, and data quality frameworks.
Experience integrating AI/agentic capabilities into engineering workflows (e.g., automation for triage, QA, runbooks, and documentation).
Background in capacity planning, cost governance, and performance engineering at enterprise scale.

Responsibilities

Set and evolve the multi-year strategy and target architecture for batch and streaming compute platforms.
Establish platform operating model, service catalogue, SLAs/SLOs, risk controls, and lifecycle standards (build, run, deprecate).
Drive platform adoption, standardization, and cost/performance optimization across workloads and environments.
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.
Provides leadership and high-level direction to teams while frequently overseeing employee populations across multiple platforms, divisions, and lines of business.
Acts as the primary interface with senior leaders, stakeholders, and executives, driving consensus across competing objectives.
Build and scale capabilities for distributed batch processing (PySpark/DBX) and real-time streaming (Flink and adjacent services).
Enable serverless execution patterns where appropriate to reduce operational burden and improve elasticity.
Ensure secure-by-design implementations including access controls, data protection, and auditability.
Serve as a senior partner to product, data, and technology leaders across Lines of Business to clarify requirements and shape demand.