Senior Product Associate - Data Management & Data Governance — Data & Analytics Agent Platform

JPMorgan Chase & Co.; Jersey City (NJ); On-site; USD 140,000 - 170,000; Full time

Job summary

JPMorgan Chase & Co. is seeking a Senior Product Associate to lead development and optimization of agentic workflows on the Data & Analytics Agent Platform.

You will translate data governance into working agents and ship scalable automation that reduces manual effort. In this role you will collaborate with cross-functional teams, shape the product roadmap through user research, and own the lifecycle of agent-based solutions from prototype to production, with strong emphasis on reliability and

Qualifications

• 3+ years in product management or a relevant domain area
• Proficient knowledge of the product development life cycle
• Experience in product life cycle activities including discovery and requirements definition
• Developing knowledge of data analytics and data literacy
• 3+ years in engineering, data platform, or technical solution delivery with hands-on experience building LLM-based or agentic apps in production
• Demonstrated ability to build against and integrate with enterprise APIs; strong REST and event-driven patterns, authentication and authorization models
• Proficiency in Python and current agent frameworks and orchestration tooling
• Experience designing evaluation and monitoring for non-deterministic systems, offline evals, regression testing and prod observability
• Proven record influencing platforms and engineering teams to build or change APIs with clear technical requirements
• Working knowledge of data management and governance concepts, metadata, lineage, and data quality

Responsibilities

• Partners with the Product Manager to identify new product opportunities from user research and discovery
• Plans upstream and downstream implications of features on the overall product experience
• Supports collection of user research, journey mapping, and market analysis to inform the strategic roadmap
• Analyzes and evaluates product metrics across the development life cycle
• Writes requirements, epics, and user stories to support development
• Be the DMDG platform's primary person for Data & Analytics Agentic Platform and ship agentic workflows
• Decompose governance processes into agent-executable task graphs and own full agent lifecycle
• Map platform APIs to workflows; compose agents and tools, handle auth, entitlements, pagination, rate limits, idempotency, error recovery
• Run an opportunity pipeline with data office and stakeholders; land agent work in platform roadmap; lead engineers
• Ensure governance by design with data handling, retention, cross-border constraints, and auditable logs

Skills

Product management

Lifecycle knowledge

Data analytics literacy

REST integration

Python

Agent frameworks

Platform APIs

Monitoring & observability

Tools

REST APIs

Agent frameworks

Python tooling

Job description

Unleash your expertise in product development and optimization by leveraging user research, analyzing metrics, and collaborating across one of the world’s most innovative financial organizations. Build and scale agentic workflows on the Data & Analytics Agent Platform, composing agents on top of existing platform APIs and driving the creation of new APIs with platform teams where capability gaps exist. The role is hands-on and delivery-led: translating data management and data governance processes into working agents, tools, and orchestrations rather than documentation. Data management and governance expertise is applied as domain knowledge that shapes what gets built the primary measure of success is agents in production that materially reduce manual effort. Act as the single accountable owner for data management and data governance across the Data & Analytics Agent.

As a Senior Product Associate in Data and Analytics Team, you contribute to the team by leveraging your expertise in product development and optimization to make a significant impact, supported by user research and customer feedback to fuel the creation of innovative products and continuously improve existing offerings. Collaborate closely with cross-functional teams and play a crucial role in shaping the future of our products and ongoing success.

Builder's instinct with a strong bias to shipping; pragmatic judgement on where agents add value and where deterministic automation is the better answer; clear technical writing for both engineering and control audiences; resilience in ambiguity as the technology and the platform evolve; collaborative influence without direct authority.
Success Measures (first 12 months), A defined portfolio of agentic workflows delivered to production, with measured reduction in manual governance effort. Reusable agent patterns and tool library adopted by at least one team beyond your own. New or extended platform APIs delivered jointly with platform teams, unblocking previously infeasible workflows. Evaluation and monitoring in place for every production agent, with published reliability baselines and trends. No material data, privacy, or access incidents attributable to agents you built; governance approvals obtained without extended remediation cycles.

Job responsibilities

• Partners with the Product Manager to identify new product opportunities that reflect the needs of our customers and the market through user research and discovery
• Considers and plans for upstream and downstream implications of new product features on the overall product experience
• Supports the collection of user research, journey mapping, and market analysis to inform the strategic product roadmap and provide insight on potential product features that provide value to customers
• Analyzes, tracks, and evaluates product metrics including work to time, cost, and quality targets across the product development life cycle
• Writes the requirements, epics, and user stories to support product development
• Serve as the DMDG platform's primary person to work on the Data & Analytics Agentic Platform. Be part of the team to design, build, test, and ship agentic workflows that automate data management and governance activities metadata capture, classification, lineage reconstruction, access-request triage, data quality triage, issue routing, control evidence collection, and policy question answering.
• Decompose multi-step governance processes into agent-executable task graphs: tool selection, prompt and instruction design, context and retrieval strategy, memory, deterministic versus LLM step boundaries, human-in-the-loop checkpoints, and fallback behavior. Own the full agent lifecycle: prototype, evaluate, harden, release, monitor, and iterate. Define evaluation sets and quality baselines before scaling, and instrument agents for cost, latency, accuracy, and intervention rate. Build reusable patterns, tool libraries, and reference implementations so other teams can compose agents without re-solving the same problems.
• Map existing platform APIs to target workflows; compose agents and tools over them, handling authentication, entitlements propagation, pagination, rate limits, idempotency, and error recovery. Identify capability gaps and partner with platform engineering to co-develop new APIs: write the use-case brief, propose contracts and schemas, specify payloads and semantics, agree SLAs, and validate against real agent workloads. Act as design partner and first consumer for new platform APIs pilot early versions, feedback on ergonomics and performance, and drive toward stable, agent-friendly interfaces. Where no API exists and none is near-term viable, define the interim path and the conditions for retiring it, avoiding durable workarounds.
• Run an opportunity pipeline with data office and business stakeholders: identify candidate workflows, size effort and benefit, prioritize, and sequence delivery. Work in close partnership with Area Product Owners and engineering to land agent work in the platform roadmap and release cycles. Lead or coach a small team of engineers and solution builders; set technical direction and raise the bar on agent build quality. Report delivery and impact metrics to senior management: workflows automated, hours displaced, adoption, agent reliability, and cost to serve.
• Apply working knowledge of data management and governance to ensure agents respect classification, entitlements, retention, cross-border constraints, and approved-use boundaries by design and never widen source-system permissions. Navigate the firm's AI/ML and data governance review requirements efficiently, preparing the artefacts needed to get agents approved and into production. Ensure agent actions, inputs, and outputs are logged and traceable to an acceptable standard for audit and control testing.

Required qualifications, capabilities, and skills

• 3+ years of experience or equivalent expertise in product management or a relevant domain area
• Proficient knowledge of the product development life cycle
• Experience in product life cycle activities including discovery and requirements definition
• Developing knowledge of data analytics and data literacy
• 3+ years in engineering, data platform, or technical solution delivery, with recent hands-on experience building LLM based or agentic applications in production, not solely prototypes or proofs of concept.
• Demonstrated ability to build against and integrate with enterprise APIs; strong practical grasp of REST and event-driven patterns, authentication and authorization models, and integration failure modes.
• Proficiency in Python (or equivalent) and current agent frameworks and orchestration tooling; comfortable with RAG patterns, vector stores, tool and function calling, and multi-agent or multi-step orchestration.
• Experience designing evaluation and monitoring for non-deterministic systems offline evals, regression testing, guardrails, and production observability.
• Proven record influencing platforms and engineering teams to build or change APIs, including writing clear technical requirements and contracts.
• Working knowledge of data management and data governance concepts, metadata, lineage, classification, data quality, entitlements, retention, sufficient to automate those processes credibly and engage governance stakeholders as a peer.

Preferred qualifications, capabilities, and skills

• Prior work automating risk, control, or governance processes in a regulated environment.
• Experience in financial services, or another industry with comparable data protection and auditability constraints.
• Exposure to cloud data platforms, Lakehouse architectures, and modern data tooling. Familiarity with AI governance expectations and applicable data protection regulation, from the perspective of getting systems approved and deployed. Experience leading small technical teams or acting as tech lead on multi workstream delivery.