Level

Whoop

Director, Data Governance

Whoop is hiring a Director, Data Governance in Boston, United States. It pays $190k-$230k a year and Level rates it ; you can apply on Whoop.

Level's read on this job

  • Level rates this role Little AI ●○○○: aI is not part of the work.
  • The posting mentions data governance, data quality and compliance among its tools and skills.
  • Level classes it as a lead role.
  • Whoop has 167 other open roles on Level.
  • Posted within the past two weeks.

AI in this role

Define and lead data governance strategy, standards, and compliance frameworks to ensure safe and trustworthy enterprise data and AI ecosystems.

data-governancedata-qualitycomplianceprivacydata-architecture

At WHOOP, we're on a mission to unlock and inspire performance for life. WHOOP empowers members to perform at a higher level through a deeper understanding of their bodies and daily lives.

WHOOP is a data company, and data is our most valuable asset. The Data, Analytics & AI (DAA) organization turns WHOOP data into trusted intelligence that sharpens every team's decisions. As the data and AI ecosystem grows, making that data trustworthy and safe by default has become a strategic capability, so every product built on it can be relied on.

We are seeking a Director, Data Governance to lead the function as a new pillar within the DAA organization. You will report to our VP of Data, Analytics and AI Acceleration. 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.

RESPONSIBILITIES

  • 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.

QUALIFICATIONS

  • 10+ years in data, engineering, or data governance roles, with a strong background in data governance, data quality, data engineering, or data platform.

  • 4+ years of experience leading and building teams.

  • Direct experience building data governance programs or frameworks, ideally in a high-growth environment where governance was not yet formalized.

  • 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).

  • 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.

Interested in the role, but don’t meet every qualification? We encourage you to still apply! At WHOOP, we believe there is much more to a candidate than what is written on paper, and we value character as much as experience. As we continue to build a diverse and inclusive environment, we encourage anyone who is interested in this role to apply.

WHOOP is an Equal Opportunity Employer and participates in E-verify to determine employment eligibility. It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.

The WHOOP compensation philosophy is designed to attract, motivate, and retain exceptional talent by offering competitive base salaries, meaningful equity, and consistent pay practices that reflect our mission and core values.

At WHOOP, we view total compensation as the combination of base salary, equity, and benefits, with equity serving as a key differentiator that aligns our employees with the long-term success of the company and allows every member of our corporate team to own part of WHOOP and share in the company’s long-term growth and success.

The U.S. base salary range for this full-time position is $190,000 - $230,000. Salary ranges are determined by role, level, and location. Within each range, individual pay is based on factors such as job-related skills, experience, performance, and relevant education or training.

In addition to the base salary, the successful candidate will also receive benefits and a generous equity package.

These ranges may be modified in the future to reflect evolving market conditions and organizational needs. While most offers will typically fall toward the starting point of the range, total compensation will depend on the candidate’s specific qualifications, expertise, and alignment with the role’s requirements.

How we rate this

Director, Data Governance at Whoop rates 30 out of 100 for how much of the daily work is AI. That makes it Little AI (AI Level 1 of 4). The level is about AI in the job, not seniority.

Classification

Little AI. AI is not part of the work.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Data GovernanceData QualityCompliancePrivacyData Architecture

Questions you could be asked

  1. Tell me about a project where data governance was part of your work. What did you do?
  2. Tell me about a project where data quality was part of your work. What did you do?
  3. Tell me about a project where compliance was part of your work. What did you do?
  4. Tell me about a project where privacy was part of your work. What did you do?
  5. Tell me about a project where data architecture was part of your work. What did you do?

Adapt your resume

  • List these exact terms on your resume: Data Governance, Data Quality, Compliance, Privacy, and Data Architecture. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.

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