Senior Data Manager

Wood Mackenzie is the global leader in analytics insights and proprietary data across the entire energy and natural resources landscape.

For over 50 years our work has guided the decisions of the worlds most influential energy producers utilities companies financial institutions and governments.

Now with the worlds energy system more complex and interconnected than ever before sector-specific views are no longer enough. Thats why weve redefined whats possible with Intelligence Connected.

By fusing our unparalleled proprietary data with the sharpest analytical minds all supercharged by Synoptic AI we deliver a clear interconnected view of the entire value chain. Our trusted team of 2700 experts across 30 countries breaks siloes and connects industries markets and regions across the globe.

This empowers our customers to identify risk sooner spot opportunities faster and recalibrate strategy with confidence whether planning days weeks months or decades ahead.

Wood Mackenzie
Intelligence Connected

Wood Mackenzie Brand Video

Wood Mackenzie Values

• Inclusive we succeed together
• Trusting we choose to trust each other
• Customer committed we put customers at the heart of our decisions
• Future Focused we accelerate change
• Curious we turn knowledge into action

Role Purpose

Were looking for an experienced data governance leader to establish and embed robust governance across our data platform with a primary focus on Snowflake. This role drives alignment between data governance architecture and engineering ensuring that governance principles are built into the platform from day one enabling trusted secure high-quality data which is optimised for advanced use cases including AI/ML models knowledge graphs and semantic frameworks.

Youll design and operationalise the governance framework (policies standards roles stewardship) and work hands-on with the data platform team to implement practical controls and tooling such as dbt and Snowflake. You will also ensure our data assets are structured enriched and governed in ways that maximise their value for AI-driven insights retrieval-augmented generation (RAG) and enterprise knowledge management. This is a high-impact position at the centre of our enterprise data strategy.

Main Responsibilities

Governance Framework & Strategy

• Define and implement the organisations data governance framework for the Snowflake data platform including policies standards stewardship and ownership models.
• Establish and chair data governance working groups or forums; provide direction on data quality lineage and metadata practices.
• Create clear roles and responsibilities for data owners stewards and consumers.
• Develop governance policies specific to AI/ML use cases including data readiness and model training data controls.

Platform Governance Enablement

• Partner with the Data Platform Owner Data Architecture and Engineering teams to embed governance controls and standards directly into the Snowflake environment.
• Design and oversee access governance (roles privileges masking data-sharing policies) using dbt and Snowflakes native features.
• Define and monitor data quality lineage and metadata management processes.
• Support the integration of data cataloguing metadata and lineage tools (DataHub).
• Develop standards for data modelling and naming.
• Establish governance standards for vector embeddings semantic layers and AI model inputs/outputs within the data platform.

AI and Knowledge Management Enablement

• Partner with AI/ML teams to ensure data is structured labelled and enriched for use in large language models (LLMs) RAG systems and generative AI applications.
• Oversee the governance of unstructured and semi-structured data (documents embeddings vectors) for AI consumption.
• Establish policies for data versioning provenance tracking and bias detection in datasets used for model training and inference.
• Collaborate on the design and governance of knowledge graphs that connect enterprise data assets enabling advanced analytics and AI-powered discovery.

Data Quality

• Lead initiatives to improve data accuracy consistency and completeness.
• Provide visibility of governance metrics including data quality KPIs platform audit results and adoption measures.
• Define data quality standards specific to AI/ML use cases.
• Implement monitoring for data and concept drift that may impact model performance.

Collaboration & Communication

• Act as a bridge between technical and business teams translating governance principles into actionable engineering and operational practices.
• Coach and mentor colleagues on data governance literacy and best practices.
• Work closely with business units to align governance with strategic data needs.
• Engage with data science AI/ML and analytics teams to understand their governance needs.

About You

• Proven experience designing and implementing data governance frameworks within an enterprise environment.
• Hands-on experience with dbt and Snowflake (e.g. schema design access roles data validation and data-sharing).
• Strong knowledge of data warehousing / modern data platform concepts (e.g. ELT dbt medallion architecture).
• Understanding of metadata management data lineage data quality and stewardship practices.
• Familiarity with cloud ecosystems (AWS).
• Excellent communication and stakeholder management skills able to engage technical engineers architects and senior business leaders.
• Ability to define governance metrics KPIs and reporting processes.
• Understanding of AI/ML data requirements including data preparation feature engineering and model governance principles.
• Knowledge of semantic data modelling ontologies taxonomies and how they support knowledge graphs and AI systems.

Desirable

• Experience with governance tooling (DataHub).
• Background in data engineering data architecture or analytics.
• Certification in data governance or data management (e.g. DAMA CDMP) is advantageous.
• Experience with knowledge graph technologies or semantic web standards.
• Familiarity with AI/ML governance frameworks responsible AI practices and model risk management.
• Understanding of LLM fine-tuning requirements prompt engineering and how data quality impacts AI outputs.
• Experience establishing data governance for generative AI use cases and managing proprietary data in AI workflows.

Expectations

As a leader you will be expected to:

• Communicate the purpose and direction of our data strategy with clarity and enthusiasm creating shared ownership
• Collaboratively develop high level plans and strategies that clearly define required outcomes and key results
• Approach business challenges with a positive and solution-orientated attitude
• Act as a mentor and peer coach
• Champion the strategic importance of governed high-quality data as the foundation for AI innovation and competitive advantage

We are a hybrid working company and the successful applicant will be expected to be physically present in the office at least 2 days per week to foster and contribute to a collaborative environment but this may be subject to change in the future.

While this is expected to be a full-time role part-time or flexible working arrangements will be considered.

Equal Opportunities

We are an equal opportunities employer. This means we are committed to recruiting the best people regardless of their race colour religion age sex national origin disability or protected veteran status. You can find out more about your rights under the law at

If you are applying for a role and have a physical or mental disability we will support you with your application or through the hiring process.