Data Governance Lead

Accountabilities:
The Data Governance Lead will build and execute the governance strategy for a modern data platform, ensuring governance standards are translated into practical engineering solutions, automated controls, and scalable processes. Build and operate an enterprise data catalog by onboarding domains and data products, defining metadata standards, and ensuring complete documentation of ownership, SLAs, classification, lineage, and contracts. Implement automated data lineage capabilities across ingestion pipelines, transformation layers, and published data products. Define and maintain data quality frameworks by creating validation rules, integrating checks into pipelines, configuring alerts, and managing remediation workflows. Configure and enforce platform-level security and governance controls, including role-based access, row- and column-level security, classification tags, masking policies, and access enforcement. Design and implement self-service access workflows that allow users to discover, request, and obtain appropriately scoped data access. Establish event contract governance practices, including schema standards, registries, retention policies, and access controls for streaming data environments. Develop governance dashboards and operational metrics covering stewardship participation, metadata completeness, lineage coverage, quality performance, policy adoption, and access request SLAs. Define and execute the enterprise data governance strategy, roadmap, standards, and operating model in alignment with business and platform objectives. Establish governance frameworks covering ownership, stewardship, quality, metadata, lineage, classification, retention, and policy lifecycle management. Implement governance requirements into platform tools, engineering processes, and delivery workflows rather than relying solely on documentation. Create and manage governance review processes for contributed data products, ensuring they meet quality, documentation, and compliance standards before publication. Design governance controls supporting AI and agent-based use cases, including data usage guardrails, sensitive data protection, traceability, and auditability. Partner with security, risk, legal, architecture, engineering, and business teams to align governance practices with enterprise requirements. Work directly with platform engineers during development cycles to review designs and implement governance controls alongside delivery teams. Support domain teams by providing guidance, standards, and feedback that enable successful adoption of federated data product practices. Build and lead a governance function over time, including developing governance engineers and stewardship capabilities as the platform matures. Requirements: The ideal candidate is an experienced data governance leader with strong technical execution skills and a proven ability to implement governance capabilities within modern cloud data environments. They combine engineering knowledge, strategic thinking, and cross-functional leadership to create practical governance solutions. Bachelor’s degree or equivalent professional experience. 8+ years of experience across data governance, data platform engineering, data architecture, analytics engineering, or related disciplines. 3+ years of experience leading governance programs, stewardship models, or cross-functional data operating frameworks. Proven experience building and operating governance capabilities within modern cloud data platforms. Hands-on experience implementing platform governance controls, including RBAC, row-level security, column-level security, classification tags, masking policies, and access management. Experience implementing and operating data catalog and lineage solutions, including metadata management and automated lineage capture. Experience defining and maintaining data quality rules within transformation pipelines and managing quality remediation processes. Experience governing event schemas and contracts in streaming or messaging environments such as Kafka, MuleSoft, or similar technologies. Experience defining data product governance standards, including ownership models, certification criteria, documentation requirements, discoverability, and lifecycle management. Experience designing governance controls for AI and agent-based workflows, including data usage policies, access controls, traceability, and auditability. Experience working directly with engineering teams through development cycles, design reviews, and implementation activities. Strong understanding of core governance disciplines, including data ownership, stewardship, metadata, lineage, cataloging, classification, retention, and policy adoption. Experience working in regulated, security-sensitive, or compliance-focused environments is highly preferred. Strong communication skills with the ability to translate technical governance concepts into practical solutions for business and engineering teams. Strong systems-thinking mindset with the ability to connect governance practices to platform architecture, delivery processes, and business outcomes. Practical understanding of how governance must evolve to support AI, automation, and emerging data-driven technologies. Benefits: Opportunity to build and shape an enterprise-level data governance capability from the ground up. High-impact role at the intersection of data platforms, governance, security, and AI innovation. Ability to work closely with engineering, business, security, and technology leadership teams. Opportunity to influence how enterprise data is managed, shared, and leveraged across the organization. Collaborative environment focused on modern data practices, automation, and scalable solutions. Professional growth opportunities through leadership responsibilities and future team-building initiatives. Flexible work environment with primarily office and computer-based responsibilities.

How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
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