Director, Product Management (Data & Agentic AI Platform)

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 design: evaluation, audit trails, approval thresholds, human-in-the-loop checkpoints, and rollback, matched to risk, including judgment on where AI should not be used
• Guide teams to rethink how they operate, not just automate existing operational steps and report progress and tradeoffs on the AI transformation to senior executives
• Make infrastructure, token, and inference cost visible and manageable for the teams that incur it, and manage vendor and model spend as part of the roadmap
• Partner with Data Engineering, InfoSec, and Compliance on building governance, lineage, quality, privacy, and access controls into the platform design

Qualifications

What you bring

• 10+ years of product management experience owning platform, data, infrastructure, including services and APIs consumed by other teams
• A platform mindset: with examples of building shared infrastructure that delivered operating leverage to the company, rather than a series of standalone point solutions
• Experience leading large data or platform migrations, and managing cost across cloud, storage, model, and vendor spend
• Knowledge of modern data architecture, including warehouses and lakehouses (such as Snowflake and Databricks), ETL/ELT, event streaming, semantic layers, metric stores, and ontologies
• Experience building data governance into a platform: lineage, quality, privacy, and access controls, ideally in a regulated business such as financial services
• Working understanding of security, fraud and regulatory risks specific to AI systems and sensitive financial data, and experience building controls for regulated businesses
• Comfort working with code, prompts, and evaluation frameworks, with the depth to earn credibility with engineering teams.
• Ability to link platform work to the business outcomes it enables and show value that is indirect rather than directly attributed
• Proven ability to influence senior stakeholders and drive outcomes without