Data Management Lead - Agentic AI for Data

Our professionals in our Corporate Functions cover a diverse range of areas from finance and risk to human resources and marketing. Our corporate teams are an essential part of our company, ensuring that we’re setting our businesses, clients, customers and employees up for success.

Make your mark by using Agentic AI to shape how teams build trusted and AI-ready data at scale as they go through the Data Development Lifecycle (DDLC). Join the Firmwide Chief Data Office at JPMorganChase where your work directly improves speed, quality, and governance in data delivery. Bring your product mindset and technical fluency to help design a platform that grows your career through high-impact, enterprise-wide collaboration.

Job Summary

As an Agentic AI Lead for the DDLC at JPMorganChase, you will help build and scale an agentic artificial intelligence solution that enables Data Product Managers to execute the end-to-end Data Development Lifecycle (DDLC) – from ideation and modeling through publishing, governance, and consumption. You will translate business needs into a clear technical roadmap, validate engineering delivery, and improve how teams publish and consume governed data products. You will partner closely with engineering and business stakeholders to define success measures, evaluate capability releases, and drive adoption across multiple business areas. 

This role sits at the intersection of business strategy, data product delivery, and software engineering execution. Your knowledge of agentic best practices will help build out the architecture for this solution including skills, tools and the harness. Your knowledge of data engineering/science best practices will help translate business needs into technical solutions. You will run structured discovery with business stakeholders to understand current workflows and friction points, then convert those insights into prioritized requirements and measurable outcomes. You will also support executive communications by synthesizing progress into concise narratives, metrics, and roadmap updates.

Job Responsibilities

• Define  end-to-end requirements for the build of an agentic solution for the data persona that enables end-to-end data product development, in alignment with firmwide data guidelines and standards.
• Lead discovery with business teams to document current workflows, identify friction points, and translate needs into a prioritized intake backlog. Also demonstrate how the agent addresses these pain points.
• Design and run structured user-feedback loops (interviews, shadowing sessions, usability tests, telemetry review) to continuously refine tool capabilities against real DPM workflows.
• Partner with engineering leads to convert business needs into well-scoped technical requirements and discrete, assignable tasks for data engineers and AI engineers.
• Validate delivered capabilities by reviewing agent behaviors, generated outputs, and integrated workflows to ensure they match intent and agreed standards.
• Define and track acceptance criteria, success metrics, and evaluation frameworks (e.g., agent task-completion rate, output quality, human-in-the-loop intervention rate) for each capability release.Coordinate delivery execution through backlog refinement, sprint planning support, release readiness checks, and dependency management across teams.
• Contribute  to the multi-year roadmap for scaling the agentic tool from PoC to a firmwide platform – including capability expansion, LOB onboarding sequencing, and platform hardening.
• Develop a working point of view on the target agentic architecture: orchestration patterns, scheduling, agent-to-agent communication, memory/state management, tool registries, and evaluation infrastructure.
• Identify reusable components, shared services, and integration points with existing firmwide data platforms and skills repositories.
• Partner with governance, risk, controls, legal, and compliance stakeholders to ensure responsible use and appropriate controls are embedded into the solution. Collaborate and align with data engineers, data scientists, product managers, architects, and technology partners to achieve business outcomes.
• Drive coordination and communication with senior stakeholders to advance the DDLC strategy and secure ongoing sponsorship. Produce executive-ready materials: roadmap updates, PoC readouts, adoption metrics, and business-value narratives.

Required qualifications, capabilities and skills

• Demonstrated understanding of agentic artificial intelligence patterns, including single-agent, multi-agent, and orchestrated approaches.
• Working knowledge of generative artificial intelligence building blocks, including large language models, prompt design, retrieval augmented generation, context design, evaluation methods, and safety controls.
• Hands-on experience using modern generative artificial intelligence development tools (for example, enterprise-approved coding assistants or agent-enabled integrated development environments).
• Working knowledge of data engineering concepts, including deployment pipelines, continuous integration and continuous delivery, environment promotion, testing strategies, and observability.
• Working knowledge of the data development lifecycle, including data modeling, schema and contract design, metadata management, data quality, lineage, and publishing standards.
• Proven ability to translate ambiguous business problems into clear technical work items, milestones, and measurable outcomes.
• Experience running pilots in a large, regulated enterprise environment, including stakeholder alignment and release discipline.
• Strong communication and presentation skills, with the ability to connect senior stakeholder priorities to engineering execution.

• Track record of end-to-end delivery in data, data science, or data product work in consulting or internal consulting contexts.

Preferred qualifications, capabilities and skills

• Experience with modern data platforms such as Databricks or Snowflake.
• Familiarity with evaluation operations for generative artificial intelligence (for example, test harnesses, benchmarking, monitoring, and continuous improvement practices).
• Advanced degree in a quantitative, computer science, or data-related discipline.
• Familiarity with financial services environments, including operating in control-conscious delivery models.