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

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.
 

• 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.

• 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.

• 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.