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

JPMorganChase•Jersey City, NJ

10d

About The Position

The Senior Product Associate is the single accountable owner for data management and data governance across the Data & Analytics Agent, working on the Data & Analytics Agent Platform. This hands-on, delivery-led role translates data management and governance processes into production agents, tools, and orchestrations, using existing platform APIs and partnering with platform teams to deliver new APIs when capabilities are missing. Success is measured primarily by production agents that materially reduce manual effort. The role combines product development and optimization with practical agent building. It involves discovering and prioritizing automation opportunities with data office and business stakeholders; designing, building, evaluating, releasing, monitoring, and improving agentic workflows; and enabling other teams to reuse patterns, tools, and reference implementations. The associate works closely with product, engineering, platform, and governance stakeholders, and may lead or coach a small team of engineers and solution builders. Agents must respect data classification, entitlements, retention, cross-border constraints, and approved-use boundaries without widening source-system permissions. The role also ensures appropriate evaluation, monitoring, logging, traceability, and governance approvals for production agents. Success measures for the first 12 months include delivering a defined portfolio of production workflows with measured reductions in manual governance effort; adoption of reusable agent patterns and tools by another team; delivery of new or extended platform APIs with platform teams; evaluation and monitoring with published reliability baselines for every production agent; and no material data, privacy, or access incidents attributable to the agents built, with governance approvals obtained without extended remediation cycles.

Requirements

At least 3 years of experience or equivalent expertise in product management or a relevant domain area.
Proficient knowledge of the product development lifecycle.
Experience with product lifecycle activities, including discovery and requirements definition.
Developing knowledge of data analytics and data literacy.
At least 3 years in engineering, data platform, or technical solution delivery, including 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 enterprise APIs; practical understanding of REST and event-driven patterns, authentication and authorization models, and integration failure modes.
Proficiency in Python or an equivalent language and current agent frameworks and orchestration tooling; familiarity 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, including offline evaluations, regression testing, guardrails, and production observability.
Proven ability to influence platform and engineering teams to build or change APIs, including writing clear technical requirements and contracts.
Working knowledge of data management and governance concepts, including metadata, lineage, classification, data quality, entitlements, and retention, sufficient to automate processes credibly and engage governance stakeholders as a peer.

Nice To Haves

Prior experience 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, particularly in getting systems approved and deployed.
Experience leading small technical teams or acting as a tech lead on multi-workstream delivery.

Responsibilities

Partner with the Product Manager to identify product opportunities based on customer needs, market conditions, user research, and discovery.
Consider upstream and downstream implications of new features on the overall product experience.
Support user research, journey mapping, and market analysis to inform the product roadmap and identify customer-value opportunities.
Analyze, track, and evaluate product metrics, including delivery against time, cost, and quality targets throughout the product development lifecycle.
Write requirements, epics, and user stories to support product development.
Serve as the Data Management and Data Governance platform’s primary contributor to the Data & Analytics Agent Platform.
Design, build, test, and ship agentic workflows to automate activities such as metadata capture, classification, lineage reconstruction, access-request triage, data quality triage, issue routing, control evidence collection, and policy question answering.
Decompose governance processes into agent-executable task graphs, defining tool selection, prompts and instructions, context and retrieval strategy, memory, deterministic versus LLM steps, human checkpoints, and fallback behavior.
Own the agent lifecycle from prototype and evaluation through hardening, release, monitoring, and iteration.
Define evaluation sets and quality baselines before scaling, and instrument agents to measure cost, latency, accuracy, and intervention rate.
Build reusable patterns, tool libraries, and reference implementations so other teams can compose agents without repeating the same work.
Map platform APIs to target workflows and compose agents and tools over those APIs, addressing authentication, entitlement propagation, pagination, rate limits, idempotency, and error recovery.
Identify platform capability gaps and partner with platform engineering to develop APIs, including use-case briefs, contracts, schemas, payloads, semantics, SLAs, and validation against real workloads.
Act as a design partner and early consumer for new platform APIs, providing feedback on ergonomics and performance and helping drive stable, agent-friendly interfaces.
Define interim paths when APIs are unavailable or not viable in the near term, including conditions for retiring those paths and avoiding durable workarounds.
Run an opportunity pipeline with data office and business stakeholders: identify candidate workflows, estimate effort and benefit, prioritize opportunities, and sequence delivery.
Partner with Area Product Owners and engineering to incorporate agent work into platform roadmaps and release cycles.
Lead or coach a small team of engineers and solution builders, set technical direction, and raise the quality of agent development.
Report delivery and impact metrics to senior management, including workflows automated, hours displaced, adoption, reliability, and cost to serve.
Apply data management and governance knowledge so agents respect classification, entitlements, retention, cross-border constraints, and approved-use boundaries, and do not widen source-system permissions.
Navigate AI/ML and data governance reviews and prepare the artifacts needed to obtain approval and deploy agents.
Ensure agent actions, inputs, and outputs are logged and traceable to standards suitable for audit and control testing.