VP, Data

Updater

7d•$275,000 - $310,000

About The Position

Updater operates at the intersection of affiliate marketing, embedded commerce, and consumer marketplaces where we create (and lose) revenue through complex, multi-party data flows. In this environment, data isn’t a support function—data runs the business. We’re seeking a VP of Data to materially elevate how we make data-driven decisions across the company. Reporting to the SVP of Engineering, this leader will architect the systems, standards, and team that transform fragmented signals into trusted, decision-enabling intelligence. This business-critical leadership role will directly impact growth, margin, risk mitigation, and executive velocity. You will build the canonical data engine powering a complex B2B2C marketplace—increasing trust, speed, and profitability through better systems and sharper insight. We’re also aggressively leaning into machine learning and AI. We’ve been developing LLM-based agentic models to help consumers purchase complex products with confidence. In addition the engineering and data teams have been utilizing Claude Code, Codex, and other LLM-based tools to increase insights and productivity. We believe strongly in being a thought leader in this space. If you’re motivated by meaningful ownership and the chance to turn complexity into competitive advantage, this role offers a rare opportunity to do exactly that.

Requirements

10+ years of experience in data engineering or data platform leadership, including prior VP / Head of Data Engineering scope (or equivalent)
Proven ownership of data systems directly tied to revenue, billing, or financial outcomes
Deep expertise in modern data architectures (batch + streaming, warehouses, orchestration, modeling)
Comfortable doing vendor evaluations, build vs buy decisions, and success implementing infrastructure projects.
Strong command of data quality, lineage, observability, and governance — with pragmatic execution
Experience stewarding data lifecycle with ML models, both traditional supervised learning as well as LLM-based models
Experience designing systems that accommodate ambiguity, delayed signals, and evolving business rules
Ability to drive clarity and alignment where metrics are contested and incentives may compete
Strong understanding of attribution, leading vs. lagging indicators, and decision-enabling data principles
Clear judgment that timely, decision-enabling truth is more valuable than theoretical perfection

Nice To Haves

Experience in affiliate marketing, marketplaces, fintech, adtech, or other revenue-complex environments strongly preferred

Responsibilities

Data Strategy & Architecture - Own the long-term data strategy in partnership with Engineering, Finance, Product, and Executive Leadership. Define and evolve the end-to-end data architecture across acquisition, transactions, fulfillment, revenue recognition, and lifecycle events. Design scalable, auditable systems that accurately model complex, multi-role KPIs while balancing speed, accuracy, and cost.
Revenue & Risk Intelligence - Build data systems that accurately models affiliate revenue-share agreements, confirmations, cancellations, and adjustments. Enable early detection of fraud and performance anomalies before they materially impact margin. Partner with Finance and cross-functional leaders to establish canonical revenue metrics and shared KPI definitions.
Time-to-Insight - Reduce the latency between business events and actionable insight. Enable faster experimentation, channel optimization, and partner performance analysis through reliable pipelines and tooling that support both real-time visibility and deep historical analysis.
Leadership & Team Management - Build and scale a high-performing data engineering organization grounded in technical excellence, data quality, and operational rigor. Foster a collaborative, high-ownership culture that empowers teams to do their best work, operate effectively in ambiguity, and deliver durable systems that accelerate business impact.
Cross-Functional Influence - Translate business ambiguity into technical clarity — and technical complexity into business understanding. Clearly articulate data definitions, tradeoffs, and system constraints. Serve as a trusted partner to Product, Engineering, Finance, and Executive Leadership in driving high-impact decisions.