Job Description
About this role:
Achieve is redefining how customers experience financial help and its internal operating model, with AI at the center rather than bolted on. We're looking for a Director of Product Management to own the two foundational enterprise capabilities that make that possible:
Data Platform - the governed data foundation and semantic layer that enable agentic workflows, natural language analytics and standardized reporting, along with the enterprise consumer data products built on it
Agentic AI Platform - the shared harness and infrastructure that lets teams across Achieve build, launch & govern their own workflows and customer-facing agents
This is initially a hands-on senior individual contributor role: you will define the platform strategy and roadmap, inform the early technology and vendor choices and work alongside engineering, data, security, risk, compliance teams to get both platforms launched and operating at scale. As the platforms mature and become more embedded into the business, we expect this role grows into a team leadership position.
Why this role matters:
A shared platform foundation enables AI at Achieve to operate at scale, so that every new agent, voice experience, and analytics capability benefits from what came before. Agents are faster to build, cheaper to operate, safer to run and better informed because they share data, context, learnings and governance. The choices made by this leader early on will shape how Achieve builds and operates for years. Done well, this role will:
Replace static dashboards and traditional BI reports with a real-time end-to-end view of the business, where people can ask questions in plain language and get trusted answers
Deliver the governed data foundation and shared agent harness that let teams design, launch, measure, and govern their own AI-driven workflows
Help teams rethink how they operate around agents, with guardrails that scale with risk, in a regulated industry where trust and auditability are non-negotiable
What you'll do
Data Platform
Define and execute strategy for the enterprise data platform, including target architecture, sequencing, and success metrics
Lead migration of existing data, ETL processes, and dashboards with minimal business disruption
Optimize replication, storage, and compute to materially reduce inefficiencies and cost
Own the roadmap for the enterprise data products and services across identity, consent, product interaction and insights that are leveraged by multiple product teams
Enable agentic and natural language analytics so business users can self-serve trusted insights
Context Engineering & Semantics
Own the enterprise semantic layer and business glossary: governed definitions that serve both BI users and agents
Define how agents get the right information, tools, instructions, and memory for each task, and how that context is assembled, versioned, tested, and kept current
Set standards for measuring context quality, including retrieval accuracy, grounding, freshness, and cost
Define access models so agents see only what each user and task allows
Agentic AI Platform
Set the vision and multi-year roadmap for the enterprise agent harness: the shared framework for building, orchestrating, evaluating, and monitoring agents
Build a small number of reference workflows that prove the harness and give other teams a model to follow
Enable teams across operations, servicing, sales, and corporate functions to build and run their own workflows on the platform, prioritized by value and feasibility
Enable new capabilities for customer-facing AI experiences and voice agents, including authentication, latency, human handoff, and compliance
Support AI-driven SDLC to measurably improve engineering throughput and quality
Track new AI frontier models, agent frameworks, and emerging standards, and translate what matters into platform decisions, including when to adopt, swap, or avoid
Governance & Change
Build safety into the platform…