Head of Data

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Head of Data based in the United States.

• Own the data strategy and roadmap, prioritizing initiatives according to their expected impact on revenue, costs, risk, and operational performance.
• Establish clear processes for intake, evaluation, and prioritization of data requests, communicating trade-offs, capacity, and priorities to leadership and the team.
• Lead the development and maintenance of the data warehouse, pipelines, and transformation models, including Google Cloud Platform, BigQuery, Dataform, and associated ingestion tools.
• Oversee dashboards, reporting infrastructure, and analytical solutions that help teams make informed, data-driven decisions.
• Develop and maintain reliable measurement and attribution capabilities, including in-house attribution models, incrementality testing, and server-side conversion tracking in collaboration with Marketing.
• Ensure customer segmentation, lifecycle analytics, and email performance analysis support customer retention and revenue growth.
• Establish robust data quality controls to identify inconsistencies and prevent inaccurate information from reaching reports, dashboards, and business decisions.
• Implement data access policies and governance frameworks that protect sensitive customer information and define appropriate permissions for employees and AI agents.
• Partner with Finance to reconcile orders and revenue across e-commerce, ERP, and financial systems while improving inventory aging analysis, demand planning, and reporting consistency.
• Evaluate the data technology stack, making defensible build-versus-buy decisions and consolidating or eliminating tools where doing so improves cost efficiency and operational performance.
• Establish a trusted semantic layer and certified business metrics so that dashboards, analysts, and internal AI-powered data tools use consistent definitions and reliable figures.
• Work closely with the CEO and senior leadership to translate analytical findings into clear recommendations that influence revenue, spending, and cost decisions.
• Lead and develop a small team spanning analytics, data engineering, and analysis, while remaining actively involved in SQL, data modeling, code review, and technical problem-solving.
• Integrate AI-assisted development tools into daily workflows, maintaining appropriate review processes, security controls, and safeguards for production data.

• At least eight years of experience in data or analytics, including three or more years leading a data function or team.
• Recent hands-on experience writing SQL and working with a modern cloud data warehouse within the past two years.
• Proven experience building data models using dbt, Dataform, or a comparable transformation framework, along with developing and maintaining data pipelines.
• Regular, practical use of AI tools in data work, including AI-assisted coding and code review, supported by appropriate quality checks and access controls.
• Demonstrated ability to influence business decisions related to revenue, profitability, or costs, with measurable outcomes that can be clearly explained and quantified.
• Experience making strategic build-versus-buy decisions and reducing technology or infrastructure costs with demonstrable savings.
• Strong executive communication skills, including the ability to manage stakeholder expectations, explain trade-offs, and align data initiatives with business priorities.
• Ability to turn complex analyses into concise recommendations, clear visualizations, and actionable insights that leadership can use.
• Broad technical versatility across analytics, experimentation, attribution, reporting, data infrastructure, and governance.
• Strong analytical reasoning and a first-principles approach to unfamiliar problems, with the ability to challenge assumptions and identify practical solutions.
• A hands-on leadership style, combining team development and strategic direction with direct technical contribution.

• Experience in regulated industries such as hemp, cannabis, alcohol, or supplements, particularly where advertising, tracking, and attribution are affected by regulatory constraints.
• Experience in e-commerce, direct-to-consumer (DTC), or other consumer-focused businesses.
• Familiarity with Shopify event tracking, particularly for headless storefronts.
• Experience with identity resolution, marketing attribution, marketing mix modeling (MMM), or incrementality testing.
• Experience with semantic-layer technologies such as Cube and cloud platforms such as Google Cloud Platform.