Title:  Data Governance Lead

We are building a modern, AI-native data platform designed around federated domain ownership, scalable governance, and automation-driven operations. This role defines how data is governed, classified, protected, and trusted across the organisation. 

You will establish the governance foundations that enable data to be safely and reliably used across analytics, automation, AI workflows, and operational systems. Your focus is on creating scalable governance models, automated guardrails, and clear lifecycle controls that support both innovation and compliance without introducing unnecessary friction. 

This is a hands-on leadership role in a fast-moving environment. You work across Product, Platform, Data, Security, CIO, and business domains to ensure that data remains discoverable, traceable, compliant, and operationally trustworthy as the platform evolves. 

Responsibilities 

• Define and implement the organisation’s data governance framework across operational, analytical, and AI-driven systems. 

• Establish standards for data classification, ownership, lifecycle management, provenance, retention, and access control. 

• Drive the implementation of federated governance models where domains retain operational ownership within centrally defined guardrails. 

• Define governance patterns for structured, unstructured, graph, and vector-based data. 

• Work closely with Data Architecture and Engineering teams to embed governance, telemetry, and lineage into core platform patterns. 

• Establish policies and automated controls for data quality, validation, metadata management, and lifecycle enforcement. 

• Define approaches for data observability, lineage tracking, auditability, and operational telemetry across the platform. 

• Collaborate with Security and Compliance teams to ensure alignment with regulatory, contractual, and internal policy requirements. 

• Guide implementation of access governance and entitlement models aligned with role-based and attribute-based access control strategies. 

• Establish governance approaches for AI-related data usage, including retrieval pipelines, training data provenance, and model interaction boundaries. 

• Drive operational maturity around data stewardship, accountability, and governance processes across business domains. 

• Support onboarding of new domains, systems, and data products into the governance framework. 

• Contribute to governance tooling selection, policy automation, and scalable compliance monitoring approaches. 

What you will work with 

• Federated data platforms spanning operational and analytical environments. 

• Structured, unstructured, graph, and vector-based data systems. 

• Metadata management, lineage, telemetry, and observability tooling. 

• Data lifecycle and retention management processes. 

• Identity, entitlement, and policy enforcement frameworks. 

• AI and retrieval-driven systems requiring provenance and explainability. 

• Regulatory, contractual, and internal governance requirements. 

• Cross-functional collaboration with Product, Data, Security, CIO, and business stakeholders. 

Artifacts & deliverables you should expect 

• Data governance frameworks, standards, and operating models. 

• Data classification, lifecycle, retention, and provenance policies. 

• Federated governance models with clear domain ownership structures. 

• Lineage, telemetry, and observability standards across core data flows. 

• Data quality and governance control definitions with automated enforcement patterns. 

• Governance onboarding processes for new systems and domains. 

• Operational dashboards and reporting for governance, compliance, and data health metrics. 

• Documentation defining stewardship responsibilities, controls, and escalation paths. 

• Guidance for AI data governance, retrieval provenance, and model interaction controls. 

Requirements 

• Strong experience in data governance, data management, compliance, or enterprise data operations. 

• Experience implementing governance models across distributed or federated data environments. 

• Practical understanding of data lifecycle management, metadata, lineage, provenance, and observability concepts. 

• Experience working with modern data platforms spanning relational, lakehouse, graph, and retrieval-oriented architectures. 

• Understanding of access governance, identity models, and policy-driven entitlement management. 

• Familiarity with governance challenges related to AI, retrieval systems, and unstructured data usage. 

• Experience defining scalable governance controls that balance compliance with operational usability. 

• Ability to work across technical and business stakeholders to establish governance accountability and adoption. 

• Strong communication skills with the ability to translate governance requirements into practical operational standards. 

• Comfort operating in environments where systems, tooling, and governance maturity are evolving rapidly. 

• A pragmatic mindset focused on scalable controls, operational trust, and automation-first governance approaches. 

How we approach data governance 

• We treat governance as an operational capability, not a documentation exercise. 

• Governance should be embedded into systems and workflows through automation and telemetry wherever possible. 

• We prioritize federated ownership with centrally defined standards and guardrails. 

• Data should remain discoverable, explainable, and traceable throughout its lifecycle. 

• Controls should enable responsible usage, not block progress unnecessarily. 

Why this role matters 

As the organisation scales its data and AI capabilities, governance becomes a foundational dependency for trust, compliance, and operational reliability. Without clear ownership, lifecycle controls, provenance, and telemetry, data quality and system integrity degrade rapidly. 

This role ensures that the organisation builds a scalable governance foundation capable of supporting analytics, automation, AI-driven workflows, and regulatory obligations without compromising agility or usability.