VP, Data Platform and Knowledge

Job Description

Job Description

Description

Role Summary

 
Wolfe is a FinTech company embedding AI across its products, processes, and day-to-day work  and the data and knowledge foundation that every AI-powered product and decision runs on needs a single, accountable owner. As VP, Data Platform & Knowledge, you are the directly responsible individual (DRI) for how Wolfe ingests, governs, structures, and surfaces information at scale you carry the outcome, not just the org chart. This is a flat team: the Senior Director and every data engineer report directly to you, and like Wolfe's other senior leaders you stay in the codebase daily, writing production code rather than only reviewing it. This is a high-trust role at the intersection of data engineering, knowledge architecture, and AI infrastructure, and it sits at the center of Wolfe's long-term competitive advantage. 

This is a 5-day onsite role in Pittsburgh, PA.

Responsibilities

• Own and build Wolfe's data and knowledge platform end-to-end ingestion pipelines, governance frameworks, vector databases, graph databases, and semantic search writing production code daily and keeping every layer production-grade, scalable, and AI-ready.
• Directly lead a flat team setting the delivery cadence and holding each person accountable to measurable results.
• Implement and enforce data quality and governance standards that run by default, removing friction for the AI and engineering teams building on the platform.
• Systematize onboarding of new data sources through a repeatable, documented process so that growth in data complexity produces clarity, not chaos.
• Partner across AI product, engineering, and business leadership to translate platform capability into business outcomes and align the organization on data standards and prioritization.
• Instrument and measure the platform data trust scores, pipeline health, and onboarding cycle time and own remediation when something breaks.

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:

• Hands-on from day one: you have personally shipped production code to the platform not just reviewed it including at least one meaningful improvement to an ingestion, governance, or retrieval component.
• The team is aligned and accountable: every direct report has a clear ownership area on the stack and a defined operating cadence for delivery and escalation.
• 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 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 of at least 50% in ad hoc data questions escalated to the platform, compared to the baseline at time of hire.

Qualifications

• 12+ 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.
• A practicing engineer who still codes daily you have never stepped away from the keyboard, and you can build and ship production data systems yourself, not just direct others who do.
• Experience directly managing a flat team spanning both senior leaders and individual engineers recruiting, developing, and holding a mixed-seniority group accountable without layers of management in between.
• Track record of building or scaling a data platform at a high-growth technology company, ideally in an AI-native environment where semantic data structure and reliability are core product requirements.
• Deep, current fluency with the modern data stack: vector databases, graph databases, embedding pipelines, and LLM-adjacent infrastructure with the authority and experience to make and defend high-stakes architectural decisions.
• Founder-level ownership mindset and bias for action: you define the outcome, build the team to deliver it, remove the blockers, and measure everything.

Compensation, Benefits, and Perks

The compensation shown includes Base Salary plus Target Incentives Bonus. In addition, you will receive RSUs. Wolfe is committed to providing a comprehensive benefits package to support your well-being, along with competitive compensation. Our benefits and perks include but not limited to:

• Restricted Stock Units (RSUs)
• Incentive Bonus
• Profit Share
• Medical, Prescription, Vision, and Dental insurance for employees and dependents (Wolfe pays 80% of premium)
• Short-Term Disability Insurance (Wolfe pays 100% of premium)
• Voluntary Long-Term Disability Insurance, Life Insurance, Critical Illness Insurance, Accident Insurance, and Hospital Indemnity coverage
• PTO (vacation and sick time)
• Corporate Holidays and Floating Holidays
• 401(k)
• Employee recognition program
• Charitable Donation to a charity of your choice yearly
• Employee Referral Bonus
• Tuition Reimbursement
• Internal Training and Information sessions
• Family Picnic, Holiday Party, and other outings
• Internal Culture Club