Director, Product Management (Data & Agentic AI Platform)

Achieve is hiring a Director of Product Management to build and scale its enterprise data and agentic AI platforms. The role owns platform strategy, roadmaps, governance, and launch, enabling teams to build AI-driven workflows and trusted analytics across the business.

Responsibilities:

• 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

Requirements:

• 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 direct authority, and to explain technical concepts to executive audiences
• In accordance with Achieve's Lead Forward Rubric, this Director position requires demonstrated excellence across four foundational leadership pillars:
• Maintains composure and steady communication during high-pressure pivots. Instills trust across teams through consistency, fairness, and transparent communication. Bridges the competing stakeholder priorities, using credibility and relationships to align divergent teams around shared goals
• Sets high-impact objectives that streamline cross-team execution and remove low-value work. Actively leads teams through change by providing context, engaging managers, addressing resistance, and adjusting plans as needed. Navigates complex ambiguity with confidence, evaluating options and providing sufficient clarity for teams to act while remaining flexible
• Drives consistently high performance through disciplined execution and clear, transparent accountability practices. Standardizes core cross-team workflows so execution remains dependable without relying on crisis management or individual heroics. Actively identifies where workflows break down between managers, replacing manual workarounds with repeatable, scalable procedures
• Identifies key talent, holds managers accountable for developing their teams, and supports active coaching. Drives strong leadership development across teams and builds capable managers. Invests in coaching, feedback, and growth opportunities to elevate capability and consistency across the function
• We will be unable to facilitate H1-B Visa transfer or sponsorship, along with STEM-OPT Visa
• Recent, demonstrated experience building agentic workflows for scaled operational use, and the ability to show what you built, what broke, and what you changed
• Experience managing model, token, and inference cost for AI workloads
• Experience with customer-facing or voice AI, including authentication, latency, human handoff, and compliance
• Experience applying AI to the software development lifecycle
• Experience mentoring or leading product managers