JPMorganChase
Website:
jpmorganchase.com
Company:
https://www.linkedin.com/company/jpmorganchase
Industries: Financial Services
Job details:
Job Description
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 the Chief Data and Analytics Organization, you lead multiple technical areas, manage the activities of multiple teams, and collaborate across technical domains. Your expertise is applied cross-functionally to evolve firm wide Data Mesh, AI/ML and GenAI, and Data Governance platform. You will play a pivotal role in driving and delivering the strategic objectives, and will collaborate with many cross-functional teams to deliver scalable, high-performance, and user-centric platform. Your expertise will guide the development of key platform components and frameworks that align with the strategic goals of the organization.
Job Responsibilities
- Define and own the delivery strategy and roadmap for large-scale, cross-team engineering programmes from planning and resourcing through risk mDefine and own the delivery strategy and roadmap for large-scale, cross-team engineering programmes from planning and resourcing through risk management, dependency tracking, and stakeholder communication serving as the primary interface between Engineering, Product, and Executive leadership on programme status, trade-offs, and escalations
- Partner with Principal Engineers and Architecture leads to translate technical vision into actionable delivery plans with clear milestones
- Lead delivery through periods of organisational or technical change, including platform migrations, team restructures, and process transitions, maintaining momentum and morale
- Accountable for quality and production stability, encompassing test automation strategy, performance benchmarking, release readiness, incident management, and SLA adherence
- Establish and refine delivery frameworks, governance models, and reporting cadences appropriate for the scale and complexity of each programme
- Identify systemic delivery risks early, including technical debt, resource gaps, and cross-team dependencies. Drive resolution before they escalate into blockers
- Drive continuous improvement in engineering delivery practices, including retrospectives, delivery metrics, cycle-time analysis, and process optimisation
- Mentor and develop technical managers and delivery leads, building a strong leadership pipeline within the engineering organisation
- Sets and scales multi-department strategy for agentic AI-enabled engineering and SDLC/TLM automation (using enterprise-authorised 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 modernisation, 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-authorised AI-assisted development and automation capabilities, to drive cross-domain reuse and measurable capacity unlock outcomes across departments
Required Qualifications, Capabilities, And Skills
- 10+ years of leadership in end-to-end technical project/programme delivery, with a total of around 20 years experience and 5+ years at Director level
- Proven track record delivering complex, multi-team programmes on time and at scale
- Comfortable navigating conversations across the full stack, including API design (REST, gRPC, GraphQL), AWS cloud infrastructure (EKS, networking, serverless, IAM), CI/CD pipelines, container orchestration (Kubernetes, Docker), event streaming (Kafka, Kinesis), CDN strategies, front-end ecosystem, observability tooling (Datadog, CloudWatch, SLOs/SLIs), databases at scale (PostgreSQL, Neo4j), security and compliance, and QA automation strategies
- Able to read and critically assess architecture diagrams and documentation, both front-end and back-end, challenge assumptions, and ask incisive questions in design reviews with technical fluency to evaluate architectural trade-offs: consistency models, service boundaries, cost vs resilience, build vs buy, and to hold engineering teams accountable for sound decisions without needing to dictate the solution
- Fluent in modern delivery methodologies (Agile, Scrum, Kanban) with the pragmatism to adapt frameworks to context rather than enforce rigid process
- Exceptional communication skills: able to distil engineering complexity into clear, concise narratives for non-technical stakeholders at all levels
- Effective at building consensus and driving outcomes across organisations with competing priorities
- Track record of building and developing high-performing delivery teams
- Skilled at influencing without authority, earning trust from engineers, product owners, and executives alike
- Experience leading multi-organisation adoption of agentic AI-enabled engineering operating models (using enterprise-authorised 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
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