Staff Product Manager, Data & Analytics

Foley is a modern SaaS company focused on safety-sensitive industries, and they are seeking a Staff Product Manager to own the data platform definition and delivery. This role involves translating executive-level data strategy into technical requirements and managing the engineering backlog to enable self-service data access across the organization.

Responsibilities:

• Own Foley's enterprise data model - the data dictionary, entity definitions, and the rules that govern how data is structured and trusted across Dynamics, Salesforce, Ordway, PlanHat, and the data warehouse (including leading the EDM working group)
• Drive the medallion architecture (Bronze to Silver to Gold) from a product definition perspective - defining quality standards, freshness SLAs, and data contracts in close partnership with engineering
• Own Silver and Gold layer data definitions - what the data means, and how it can be consumed by applications, agents, and self-service users
• Prioritize the engineering backlog in partnership with the VP of Engineering - making trade-off calls with full understanding of technical implications
• Be the primary data platform partner for business stakeholders across Growth, Sales, Customer Success, Finance, and Operations - enabling self-service access rather than fulfilling individual data requests
• Own stakeholder communication: proactive status, and crisp escalation of blockers
• Maintain a weekly touchpoint with the AI Value Creation Office (VCO) - alignment between data platform and AI/data science needs must be exceptionally tight; the AI VCO owns governance and enablement, and this role co-ordinates the technical work that supports it
• Own delivery against quarterly KRs - track dependencies, surface risks early, and keep work moving across engineering, the analyst, and the AI VCO org
• Own the product roadmap for Foley's intelligence self-service platform - the Gold layer as an open consumption surface, the API layer, Chat on Data, dashboards, and paved paths for self-service access
• Own the API layer as a product - defining what programmatic access looks like, how it is governed, and how applications and downstream services consume the Gold layer
• Define and maintain the metric layer: conformed metric definitions that serve as the single source of truth for business KPIs across revenue, compliance, and customer health
• Partner with the data analyst and engineering to enable self-service intelligence - the goal is democratized access, not a centrally managed BI function
• Drive platform adoption and capability enablement across the organization; identify gaps in paved paths and self-service coverage
• Own the capabilities and paved paths backlog - the roadmap is platform capabilities, self-service enablement, and infrastructure that unlocks access, not individual reports or data requests
• Anticipate blockers before they become misses - not after
• Ramp quickly on Foley's business, customers, user personas, and the regulatory data landscape (FMCSA, DOT compliance, driver qualification) to make context-informed technical decisions

Requirements:

• Strong technical depth in data - you have either built data systems yourself or worked closely enough with data engineering teams to own architecture trade-off decisions with confidence
• Deep familiarity with data architecture concepts: medallion architecture, data contracts, entity resolution, CDC, API layer design, and pipeline patterns
• Hands-on familiarity with modern data tooling: dbt, AWS storage solutions (Redshift, S3, Bedrock), graph databases, and pipeline development patterns
• Ability to write precise technical requirements - engineers should be able to execute against your specs without follow-up clarification
• Comfort with the self-service / distributed intelligence model - you think in platforms and paved paths, not reports and data requests
• Strong written and verbal communication - crisp, honest status; no sugarcoating risk; clear writing for both technical and executive audiences
• Demonstrated ability to ramp quickly in a new domain and earn credibility with engineering through knowledge, not just process
• Ownership mindset: accountable for outcomes, not just coordination
• Former data engineer or analytics engineer who transitioned into product management
• Experience building or owning a self-service data platform - API layer, democratized access, paved paths for consumers
• Experience in a compliance, regulatory, or data-as-a-product company where data quality carried real business consequences
• Familiarity with Amazon Neptune, graph data models, or entity resolution concepts
• Experience working at the intersection of data and AI - comfortable with the tight dependency between data strategy and ML/data science
• Experience in a PE-backed, fast-paced environment where priorities shift and resourcefulness matters