VP, Data Platform and Knowledge

Wolfe is building the data and knowledge foundation that every AI-powered product, team, and decision in the company will run on and we’re looking for a VP-level leader to own it. As VP of Data Platform & Knowledge, you will set the strategic direction for how Wolfe ingests, governs, structures, and surfaces information at scale. This is an executive-level, high-trust role at the intersection of data engineering, knowledge architecture, and AI infrastructure one that sits at the center of Wolfe’s long-term competitive advantage.

You will work directly with the C-suite and cross-functional leadership to make our data trustworthy, our AI teams self-sufficient, and our platform ready to grow as fast as the business demands. You’ll bring a founder’s sense of urgency and ownership, operating with the authority to make decisions, the accountability to deliver outcomes, and the influence to align the entire organization around a shared data standard. This is not a hands-on engineering role.

You will set direction, attract and lead talent, and hold the organization accountable to results. This is a 5-day onsite role in Pittsburgh, PA. Own the enterprise-wide data and knowledge platform strategy including ingestion pipelines, governance frameworks, vector databases, and semantic search infrastructure ensuring every layer is production-grade, scalable, and AI-ready.

Define, enforce, and evangelize data quality and governance standards that operate by default, not by committee, eliminating friction for AI and engineering teams building on the platform. Serve as a key executive stakeholder across AI product, engineering, and business leadership translating platform capability into business outcomes and ensuring organizational alignment on data standards and prioritization. Build, grow, and retain a high-performing team of data and knowledge engineers, setting a culture of velocity, ownership, and measurable accountability at every layer of the stack.

Drive the architecture and expansion of Wolfe’s knowledge foundations including systematic onboarding of new data sources so that growth in data complexity creates clarity, not chaos. Impact Statement: For more clarity on the role, below are the success metrics and measurements for this role in the first 90 to 120 days. Data trust is quantified, not assumed: A data quality scoring system is live across all core data sources, with at least 90% of priority datasets rated and documented and a defined SLA for how quickly a data quality issue is identified, escalated, and resolved (target: under 24 hours from detection to remediation).

New source onboarding is systematized and proven: A repeatable ingestion onboarding process is documented and has been successfully used to bring at least one net-new data source from scoping to production in 30 days or fewer, with zero regression to existing pipelines. AI teams are unblocked and self-sufficient: At least two active AI product teams can independently identify which data sources to rely on for their use case measured by a reduction in ad hoc data questions escalated to the platform team by at least 50% compared to baseline at time of hire. 10+ years of progressive experience in data platform, data engineering, or knowledge infrastructure with at least 5 years in a senior leadership role owning a team, a budget, and a multi-year roadmap.

Requirements

Deep fluency in modern data stack components including vector databases, embedding pipelines, and LLM-adjacent infrastructure with the authority and experience to make high-stakes architectural decisions and hold engineering teams accountable to them.