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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Governance Lead - **Company:** REFLECTION LLC - **Location:** San Francisco, CA, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Training Data, Artificial Intelligence, Amazon Web Services, Microsoft Azure, BigQuery, Cloud Database, Data Validation, Data Governance, Data Warehousing, Meta-Data Management, Snowflake, Data Lineage - **Published:** May 31, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=e9bc264ce4363f97 ## About the Role Do you have experience in Technical documentation?, * 5+ years in data governance, data privacy, or a closely related discipline - with meaningful experience at a technology company handling large-scale or sensitive datasets. * Hands-on experience conducting and owning DPIAs, privacy assessments, and data protection documentation - ideally in a context where these were reviewed by external auditors or regulators. * Deep working knowledge of GDPR, CCPA/CPRA, and the EU AI Act - and the ability to translate regulatory requirements into concrete, operationalizable policies and controls. * Experience with training data provenance, dataset licensing, and consent management in an ML or AI context - you understand why labeler provenance and data lineage matter for model accountability, not just compliance. * Familiarity with compliance-as-code approaches: you've worked with or built automated data validation gates, policy-enforcement pipelines, or pre-deployment checks tied to data quality and compliance metadata. * Technical fluency with cloud data infrastructure (AWS, GCP, or Azure), data warehouses (BigQuery, Snowflake), and data cataloging or lineage tools - enough to design controls and engage credibly with engineering teams. * Experience building and maintaining risk registers, evidence stores, and audit documentation - you know what "regulator-grade" evidence looks like in practice. * Demonstrated ability to drive cross-functional alignment across Legal, Security, Research, and Engineering, including influencing without formal authority in a fast-moving environment. * A builder's mindset: you're energized by 0 1 work, comfortable creating structure where little exists, and pragmatic enough to ship imperfect-but-useful processes on the way to ideal ones. * Relevant certifications (CDMP, CIPP/E, or similar) are a plus - but track record and demonstrated impact matter more than credentials. ## Description * Own dataset provenance, training-data summaries, DPIAs, and the privacy and compliance posture of Reflection AI's training and evaluation data - so that every model we ship has auditable, regulator-grade evidence of its data lineage, licensing, privacy posture, and risk mitigations. What You'll Do * Produce audit-ready data provenance records and training-data summaries for every production model - documenting origin, transformations, labeler provenance, and data quality so we can satisfy auditors, enterprise customers, and regulators on demand. * Own Data Protection Impact Assessments (DPIAs) end-to-end: drive them to completion with Legal, and publish DPIA outputs alongside model documentation to meet EU AI Act and GDPR expectations. * Enforce prohibited-source and license controls at data intake - preventing risky or non-compliant data from ever reaching a training run - and maintain a verified provenance and approval log for all vendor datasets. * Keep the company DSAR-ready by producing lineage reports that map model outputs back to source data and subject controls, enabling timely and accurate responses to data subject requests. * Assemble and maintain defensible evidence bundles - data manifests, DPIAs, consent and license records - into the enterprise evidence store so that audits and customer security reviews are straightforward and fast. * Log data findings in the risk register, drive remediation with the relevant owners, and report residual risk to governance forums and senior leadership on a regular cadence. * Partner with Research, Engineering, Legal, and Security to establish data ownership structures, access controls, and stewardship practices across all training, evaluation, and internal data assets. * Champion a culture of data literacy and responsible data use - building runbooks, intake checklists, and guidelines that help teams make confident, compliant decisions without bottlenecking on you ## Related Videos - [Data Governance in the Era of AI](https://www.wearedevelopers.com/videos/1622-data-governance-in-the-era-of-ai) - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [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) - [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) - [Making Data Warehouses fast. 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