Data Governance Engineer

About the role

Abacus Insights is transforming how data works for health plans. Our mission is simple: make healthcare data usable, so the people responsible for care and cost decisions can act faster, with confidence. We help health plans break down data silos to create a single, trusted data foundation. That foundation powers better decisions -so plans can improve outcomes, reduce waste, and deliver better experiences for members and providers alike. Backed by $100M from top investors, we're tackling big challenges in an industry that's ready for change. Our platform enables GenAI use cases by delivering clean, connected, and reliable healthcare data that can support automation, prioritization, and decision workflows-and it's why we are leading the way. Our innovation begins with people. We are bold, curious, and collaborative-because the best ideas come from working together. Ready to make an impact? Join us and let's build the future together. We are seeking a Data Governance & AI Trust Manager to define and operationalize the governance framework that enables high‑quality, well‑governed data to safely and effectively power GenAI agents, copilots, and analytics across the company. As the organization accelerates adoption of Generative AI, the quality, structure, provenance, and clarity of corporate operating data have become critical enablers of AI performance, trustworthiness, and value. This role exists to ensure that the company's operating data ecosystem is intentionally designed, consistently governed, and fit for AI‑enabled use cases, rather than fragmented, ambiguous, or ad‑hoc. This role reports to the CISO & CIO and operates as a cross‑functional data governance authority across all company content. It does not own or manage the data itself; content ownership remains with business and platform owners. Instead, this role defines the standards, policies, and operating expectations under which data is created, managed, shared, retained, and made eligible for analytics and GenAI use. A core responsibility of the role is to set the vision for the company's internal data ecosystem-including how platforms such as SharePoint, Confluence, Jira, Slack, Teams, and related systems should be used together-and to establish clear operational standards (for example, when content belongs in SharePoint versus Confluence, and how that decision impacts AI use). Governance is expected to be operationalized through native platform controls and existing enterprise tooling, rather than through standalone or heavyweight governance systems. The role translates governance principles into practical, enforceable standards that improve data quality, reduce ambiguity, and materially improve GenAI outcomes. This is not a traditional data engineering or compliance analyst role. Success requires strong program ownership, the ability to drive cross‑functional alignment without direct authority, and sound judgment at the intersection of data quality, AI trust, security, and business operations. Your Day‑to‑Day Governance Framework Design & Ownership Own and evolve the company's Corporate Operating Data Governance framework Define governance standards for unstructured and semi‑structured data across collaboration and productivity platforms Establish clear ownership models (Data Owners, Data Stewards, Custodians) for operating data domains Ensure governance practices enable business velocity rather than obstruct it Operationalize governance through native platform capabilities (e.g., metadata, permissions, retention, labeling, workflows) rather than standalone governance tooling Metadata, Classification & AI Eligibility Define and maintain metadata standards, including: Data classification Purpose of use Lifecycle state AI eligibility and exclusion rules Partner with Security and Legal to ensure alignment with privacy, regulatory, and contractual obligations Embed metadata and classification standards directly into existing platforms rather than creating parallel systems Data Quality & Trustworthiness Establish fitness‑for‑purpose data quality expectations for corporate operating data Define quality signals for: Accuracy Completeness Timeliness Consistency Partner with business teams to identify high‑risk or high‑impact data domains Ensure AI‑enabled experiences are grounded in authoritative, explainable, and traceable data sources Lifecycle, Retention & Risk Management Define lifecycle expectations for operating data, including creation, maintenance, archival, and deletion Coordinate retention and defensibility standards to support legal, compliance, and audit needs Reduce long‑term risk from unmanaged or over‑retained unstructured data Support eDiscovery and investigation readiness through clarity and traceability Cross‑Functional Execution & Enablement Drive accountability across Product, Engineering, Security, Legal, IT, and Business teams Establish practical governance cadence

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