at J.P. Morgan
Bulge Bracket Investment BanksPosted 4 days ago
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**Senior Director of Software Engineering** at JPMorganChase drives strategic decisions for batch and streaming compute platforms. Lead multiple teams, define platform standards, and drive adoption. Key responsibilities include setting multi-year strategy, establishing operating models, optimizing costs, and ensuring secure, compliant implementations. Candidate requires 10+ years in software engineering and 5+ years leading complex platforms. Must have deep expertise in distributed data processing (PySpark, Flink) and platform tooling (DBX). Proven ability to lead cross-functional delivery and operate in high-control environments.
- Compensation
- Not specified
- City
- Jersey City
- Country
- United States
Currency: Not specified
Full Job Description
Location: Jersey City, NJ, United States
Your opportunity to make a real impact and shape the future of financial services is waiting for you. Lets push the boundaries of what's possible together.
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.
Job responsibilities
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.
Job 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.
- Lead multiple engineering teams and/or platform pods delivering core compute services, libraries, and developer experience.
- Own roadmap planning, prioritization, delivery governance, and measurable outcomes (availability, latency, throughput, cost, and developer productivity).
- Define reference patterns for data processing, orchestration, observability, lineage, and reliability engineering.
- 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.
- Integrate compute platforms with firmwide AI frameworks to support AI-assisted data engineering and operational workflows. Develop domain agents that support the data processing lifecycle (intake, transformation, validation, monitoring, incident triage, remediation, documentation). Partner with governance and control stakeholders to ensure compliant usage and appropriate guardrails.
- Serve as a senior partner to product, data, and technology leaders across Lines of Business to clarify requirements and shape demand. Translate business needs into an executable portfolio; resolve trade-offs and dependencies across teams. Communicate effectively to executive stakeholders with crisp narratives, metrics, and decision-ready options.
Required qualifications, capabilities, and skills
- 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
- Senior engineering leadership experience owning large-scale platforms (not just applications) with measurable reliability and adoption outcomes.
- 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.
Preferred qualifications, capabilities, and skills
- 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.
