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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Director, Data Governance - **Company:** Whoop, Inc. - **Location:** Boston, MA, United States - **Experience:** Experienced - **Salary:** $190,000.0 - $230,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Data Analysis, Automation of Tests, Cyber Security, Information Engineering, Data Governance, Data Infrastructure, Data Warehousing, Role-Based Access Control, Cloud Services, Data Classification, Snowflake, Data Strategy, Dynamic Data - **Published:** September 5, 2026 - **Apply:** https://startup.jobs/director-data-governance-whoop-com-9923642 ## About the Role * 10+ years in data, engineering, or data governance roles, with a strong background in data governance, data quality, data engineering, or data platform, and at least 4 years leading and building teams. * Direct experience building data governance programs or frameworks, ideally in a high-growth environment where governance was not yet formalized. * Deep hands-on experience with modern data stack tooling: Snowflake, dbt, Airflow/orchestration, and data catalog/lineage tools. * Working knowledge of healthcare data regulations (HIPAA), or willingness to develop deep expertise quickly given the WHOOP expansion into regulated healthcare products. * Experience implementing data quality frameworks with automated monitoring and alerting. * Experience with access control design and administration in cloud data platforms (Snowflake RBAC, row-level security, dynamic data masking). * Demonstrated ability to drive cross-functional alignment on data standards and policies, and to lead highly cross-functional initiatives. Comfortable influencing engineering, product, and business teams. * Experience operating in regulated environments. * Proven track record managing data oversight for complex research initiatives, such as IRB-regulated studies or trials or regulatory filings for the FDA. * Excellent executive communication and organizational leadership. Able to translate technical governance concepts into business value for non-technical stakeholders and senior leadership. * A player-coach orientation. Willing to be deeply hands-on while building repeatable processes and growing a team. * Strong commitment to embracing and leveraging AI tools in day-to-day tasks, ensuring AI-assisted work aligns with the same high-quality standards as personal contributions. *This role is based in the WHOOP office located in Boston, MA. The successful candidate must be prepared to relocate if necessary to work out of the Boston, MA office. ## Description We are seeking a Director, Data Governance to stand up and lead Data Governance as a new pillar within DAA, reporting to the VP of Data, Analytics and AI. As the first dedicated leader of this function, you will define the strategy, operating model, and roadmap for data ownership, contracts, event taxonomy, cataloging, access policy, data quality standards, and privacy and compliance. Governance sets the policy and standards; the Data Platform & Engineering pillar engineers and implements them, so this role leads through influence, clear standards, and strong cross-functional partnership. WHOOP operates a mature data platform. This role builds the governance layer on top of it: formal ownership models, data contracts, an event taxonomy, cataloging and discovery, classification frameworks, and the policies that let the company scale into regulated environments without slowing down. You will build and lead a team and champion intelligent, AI powered approaches to continuously monitor the health and trust of our data. You should be equally comfortable setting strategy and rolling up your sleeves, and as effective presenting a data classification framework to senior leadership as you are working through a data quality standard with an engineer. Working across Data Platform & Engineering, Analytics, Data Science, Product, GRC, Legal, InfoSec, and RAD, you will earn trust across technical and business teams, drive adoption, and ship enforceable governance outcomes., * Define and lead the data governance strategy, operating model, and roadmap spanning data ownership, contracts, event taxonomy, cataloging, access policy, data quality standards, and privacy and compliance (HIPAA, GDPR, CCPA), aligned with the DAA strategy. * Build, lead, and coach a small, senior team, developing your people while partnering closely with Staff level technical leaders across the org. * Establish a formal data ownership model, partnering with engineering and business stakeholders to assign stewardship and accountability for critical datasets. * Define the standards for data contracts between producing and consuming teams, covering schema stability, SLA adherence, and breaking-change management, with implementation owned by Data Platform & Engineering. * Own the event taxonomy governance framework, bringing structure to event tracking and standardizing how product and engineering teams define, name, and instrument events across platforms. * Set data quality standards (freshness, validation, monitoring) and the incident response expectations, partnering with Platform to operationalize automated testing and anomaly detection. * Champion AI powered governance approaches that proactively surface quality issues, ownership gaps, policy violations, and compliance risks. * Partner with GRC, Legal, and InfoSec to define the privacy, HIPAA, and compliance standards for how sensitive data is handled, which the platform then implements. * Define role-based access policy across the data platform, balancing security with self-service accessibility, with enforcement engineered by Platform. * Embed governance standards directly into engineering and developer workflows so the right way is the easy way, rather than relying on manual processes. * Develop governance metrics and reporting to track adoption, compliance, and data health across the organization. * Build a culture where trusted data is a shared responsibility across technical and business functions. ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Data Governance in the Era of AI](https://www.wearedevelopers.com/videos/1622-data-governance-in-the-era-of-ai) - [Dynamic Entities in .NET: Building Low-Code Systems on Top of Entity Framework Core](https://www.wearedevelopers.com/videos/100218-dynamic-entities-in-net-building-low-code-systems-on-top-of-entity-framework-core) - [How Cisco embraced a DevOps culture within its network engineering team](https://www.wearedevelopers.com/videos/99-how-cisco-embraced-a-devops-culture-within-its-network-engineering-team) - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) - [Giving AI eyes: How to build a dashboard you can't see](https://www.wearedevelopers.com/videos/100193-giving-ai-eyes-how-to-build-a-dashboard-you-can-t-see) ## Related Articles - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [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) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Panel Discussion: Responsible AI in Practice - Real-World Examples and Challenges](https://www.wearedevelopers.com/magazine/488-panel-discussion-responsible-ai-in-practice-real-world-examples-and-challenges) - [Fully Remote Software Engineer Jobs](https://www.wearedevelopers.com/magazine/447-fully-remote-software-engineer-jobs)