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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # VP, Data Platform and Knowledge - **Company:** Wolfe, Llc - **Location:** Pittsburgh, PA, United States - **Experience:** Expert - **Salary:** $200,000.0 - $226,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Information Engineering, Data Infrastructure, Data Sharing, Data Structures, Software Product Management, Search Technologies, AI Infrastructure, Data Ingestion, Large Language Models - **Published:** June 7, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=7dccf33dcde441eb ## About the Role Do you have experience in Technical architecture?, * 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. * Executive-level track record of building or scaling a data platform at a high-growth technology company, ideally in an AI-native or AI-first environment where semantic data structure and reliability are core product 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. * Proven ability to operate at the executive level: aligning C-suite stakeholders, communicating complex platform tradeoffs in business terms, and driving company-wide adoption of data standards through influence rather than mandate. * Founder-level ownership mindset you define the outcome, build the team to deliver it, remove the blockers, and measure everything. ## Description 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. ## Related Videos - [Smart City, Smart Mobility](https://www.wearedevelopers.com/videos/954-smart-city-smart-mobility) - [When React Meets Reality: Building a Real-Time Control Room for Autonomous Vehicles](https://www.wearedevelopers.com/videos/2091-when-react-meets-reality-building-a-real-time-control-room-for-autonomous-vehicles) - [Phel, a native Lisp for PHP](https://www.wearedevelopers.com/videos/791-phel-a-native-lisp-for-php) - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [Building Accountability in Agentic AI](https://www.wearedevelopers.com/videos/100046-building-accountability-in-agentic-ai) - [AI Vector Search at Scale - Ewa Szyszka - Ewa Szyszka](https://www.wearedevelopers.com/videos/2161-ai-vector-search-at-scale-ewa-szyszka-ewa-szyszka) ## Related Articles - [Stephan Gillich - Bringing AI Everywhere](https://www.wearedevelopers.com/magazine/489-stephan-gillich-bringing-ai-everywhere) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer)