Head of Data

United StatesRemoteFull-timeexecutive

DataData Manager

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Quick Summary

Overview

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.

Technical Tools

DataData Manager

The Head of Data will lead the organization's data function end to end, combining strategic leadership with hands-on technical execution to turn data into measurable business outcomes. This role is ideal for an experienced data leader who thrives on solving complex problems, building scalable infrastructure, and directly influencing revenue, profitability, and operational efficiency. You will oversee data strategy, engineering, analytics, reporting, attribution, and governance while prioritizing initiatives according to their financial and business impact. Working closely with executive leadership, Finance, Marketing, Operations, and a small multidisciplinary team, you will establish trusted data systems that support better decision-making across the business. You will also champion the practical use of AI tools to accelerate development, improve productivity, and scale the team's capabilities without unnecessary complexity. In a fast-growing, highly regulated consumer environment, you will help build a reliable, cost-effective, and secure data ecosystem that adapts to changing business and compliance requirements.

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.

Requirements

~2 min read

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