Director, Data Governance, Quality and Enablement

Madrigal is a biopharmaceutical company focused on delivering novel therapeutics for metabolic dysfunction-associated steatohepatitis (MASH), a serious liver disease that can progress to cirrhosis, liver failure, need for liver transplantation and premature mortality. Every member of our Madrigal team is connected by our shared purpose: leading the fight against MASH.

Madrigal’s medication, Rezdiffra (resmetirom), is a once-daily, oral, liver-directed THR-β agonist designed to target key underlying causes of MASH. Rezdiffra is the first and only medication approved by both the FDA and European Commission for the treatment of MASH with moderate to advanced fibrosis (F2 to F3). An ongoing Phase 3 outcomes trial is evaluating Rezdiffra for the treatment of compensated MASH cirrhosis (F4c).

Our success is driven by our people. We are building a dynamic, inclusive, and high-performing culture that values scientific excellence, operational rigor, and collaboration. To support our continued growth, we are strengthening our workforce strategy to ensure we have the right talent, at the right time, in the right way.

The Director, Data Governance, Quality & Enablement will lead the development and execution of capabilities that ensure enterprise data is trusted, compliant, high-quality, discoverable, and accessible across the organization. This role will lead the implementation of data governance, data quality, metadata, lineage, cataloging, data product controls, and data enablement capabilities across the enterprise data ecosystem.

The Director will partner closely with Data Engineering & Platform, Data Products & Analytics, Enterprise Integration, IT Security, Privacy, IT Compliance, and business Data Owners and Stewards to embed governance and quality directly into data pipelines and data products. The role will enable trusted data consumption across analytics, operational integrations, automation, and AI use cases, while ensuring appropriate controls and standards are consistently applied across the enterprise data lifecycle.

Data Governance Technology & Operating Model Develop and execute the IT data governance roadmap in alignment with enterprise data strategy and business priorities. Establish technical governance standards and controls across the enterprise data platform. Implement scalable governance capabilities for data classification, ownership, metadata, lineage, quality, access, and lifecycle management. Partner with business functions to operationalize Data Owner and Data Steward responsibilities.

Translate enterprise policies, regulatory requirements, and business expectations into practical technology standards and controls. Establish governance processes that enable rapid delivery while maintaining appropriate controls.

Data Quality & Observability Establish the enterprise technology framework for data quality and data observability. Define reusable patterns for profiling, validation, monitoring, reconciliation, and exception management. Partner with Data Engineering teams to embed automated quality controls directly into pipelines. Establish quality gates across ingestion, transformation, and data-product publication. Implement monitoring and scorecards for critical data products and Critical Data Elements. Drive root-cause analysis and remediation of systemic data-quality issues.

Establish measurable data-product health and reliability standards.

Metadata, Catalog & Lineage Lead implementation and adoption of enterprise metadata, catalog, and lineage capabilities. Establish technical metadata standards across pipelines, lake houses, semantic models, integrations, and data products. Enable automated lineage from source systems through transformation to downstream consumption. Partner with business Data Stewards to connect technical metadata with business definitions and context. Improve enterprise data discovery and reuse through searchable, governed data catalogs.

Governed Data Products Establish the technical standards required for a data asset to become a certified enterprise data product. Define minimum requirements for ownership, metadata, lineage, quality, security classification, documentation, SLAs, and lifecycle management. Partners with Data Engineering and Data Products & Analytics teams to embed these requirements into development processes. Establish automated certification and quality controls wherever practical. Promote reuse of trusted enterprise data products rather than…