VP Data Engineering in Edinburgh

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 world’s most influential energy producers, utilities companies, financial institutions and governments. Now, with the world’s energy system more complex and interconnected than ever before, sector-specific views are no longer enough. That’s why we’ve redefined what’s 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 2,700 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.

Role Summary

The Vice President of Data Engineering is responsible for defining, building, and scaling a modern, enterprise-wide data engineering capability within a federated operating model. This role will lead the design and delivery of robust, secure, and high-performing data pipelines, with a strong focus on AWS-native architectures and Snowflake-based data warehousing. The VP of Data Engineering will establish best-in-class engineering practices, enabling domain-oriented data ownership while ensuring consistency through shared standards, governance, and platform capabilities. A critical aspect of the role is enabling the development of AI-ready data ecosystems, including knowledge graphs, ontologies, and semantically enriched datasets that support advanced analytics, machine learning, and AI-native applications.

Role Responsibilities

• Define and execute the enterprise data engineering strategy aligned to a federated (data mesh-style) operating model, balancing domain autonomy with centralized governance.
• Build, scale and lead a high-performing data engineering organization, including platform, enablement, and domain-aligned teams.
• Architect and oversee scalable, secure data platforms leveraging AWS services (e.g. S3, Glue, Lambda, EMR, Redshift), dbt and Snowflake.
• Establish best practices for data ingestion, transformation, orchestration, and serving (batch, streaming, and real-time patterns).
• Drive adoption of modern data engineering principles including DataOps, CI/CD, infrastructure-as-code, and automated testing frameworks.
• Define and enforce data governance standards, including data quality, lineage, cataloging, security, and compliance across federated domains.
• Enable self-service data capabilities through reusable data products, shared tooling, and developer platforms.
• Lead the design and implementation of AI-native data architectures, including feature stores, vector databases, and semantic layers.
• Champion the creation and integration of knowledge graphs and ontologies to enhance data discoverability, interoperability, and contextual understanding.
• Collaborate with senior stakeholders across engineering, product, analytics, and AI/ML teams to deliver business value through data.

Key Skills and Experience

• Proven experience leading large-scale data engineering organizations in complex, federated or matrixed environments.
• Deep expertise in AWS data ecosystem (S3, Glue, Lambda, Kinesis, EMR, IAM, Lake Formation) and cloud-native architecture patterns.
• Strong hands-on and architectural experience with Snowflake/dbt/Airflow, including performance optimization, data modelling, and cost management.
• Expertise in building scalable modern data platforms (data lakes, lakehouses, and data warehouses) enabling reliable real-time and batch analytics.
• Strong understanding of distributed data processing frameworks (e.g. Spark, Flink) and streaming technologies.
• Demonstrated implementation of DataOps practices, including CI/CD pipelines, observability, testing, and automated deployments.
• Experience designing and operationalizing data governance frameworks in a federated or data mesh environment with self-service and trusted data capabilities.
• Highly versed in delivering ML/AI-ready ecosystems (feature stores, semantic layers, graph databases) aligned with executive stakeholders to drive business impact.
• Practical experience with knowledge graphs, ontologies, semantic modelling (e.g. RDF, OWL), delivering faster insights.
• Strong leadership, stakeholder management, and communication skills, with the ability to influence at executive level and drive organizational change.

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 www.eeoc.gov. 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.