01Overview
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 Data Governance Engineering Lead at JPMorgan Chase in the Chief Data Analytics Office, you will lead complex, multi-functional technology projects and programs that will impact experiences for multiple groups across the firm, including clients, employees, and stakeholders. With your advanced analytical reasoning and adaptability skills will enable you to break down business, technical, and operational objectives into manageable tasks, while navigating through ambiguity and driving change. With demonstrated technical fluency, you will effectively manage resources, budgets, and cross-functional teams to deliver innovative solutions that align with the firm's strategic goals. Your exceptional communication and influencing abilities will foster productive relationships with stakeholders, ensuring alignment and effective risk management. In this pivotal role, you will contribute to the development of new policies and processes, shaping the future of our technology landscape
Job responsibilities
Manage the Data Governance roadmap across the organization, ensuring alignment with evolving global privacy strategic priorities, and lines of business.
Lead key Firmwide privacy-centric initiatives such as consent management, data retention, cross-border data transfer governance
Responsible for the structure of roadmap delivery and operating model across the organization
Oversee engineering projects, risks, issues, and dependencies across the Data Governance book of work,
Develop and maintain data governance metrics, KPIs, and dashboards to provide executive-level visibility into the firm's privacy posture, incident trends, and regulatory compliance status
Prepare and deliver comprehensive reports and presentations to C-Level Executives, including the Operating Committee, to communicate program status, risks, and achievements
Partner with the product organization to drive business outcomes, ensuring that technical programs are aligned with strategic business goals
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
10+ years of leadership in building technology applications and architecture with an overall of around 20 years experience and 5+ years at Director level
Strong understanding of global data governance regulations to drive technical architecture decisions and engineering requirements
Strong experience in program management, stakeholder management and building technical data platforms using AWS
Experience with enterprise-scale implementations of cloud-native data platforms such as Databricks and Snowflake
Experience navigating complex data governance, security, and compliance requirements across multi-cloud and hybrid data environments at enterprise scale with demonstrated proficiency in technical solutions, vendor product knowledge, managing vendor relations, and implementing solutions
Knowledge of records retention, legal holds, and defensible disposal across structured and unstructured data, including backups and snapshots
Proven track record deploying and governing enterprise metadata, catalog, and lineage platforms plus practical expertise with data contracts and schema governance
Demonstrated ability to evaluate and drive technical decisions regarding data platform trade-offs including performance optimization, cost management, scalability, and operational excellence
Technical understanding of modern data platform architectures including data lakes, data warehouses, lake house architectures, and distributed computing frameworks
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 .
