VP Data Engineering

5d5 days ago

Wood Mackenzie

Edinburgh, GB · Full-time · £150,000 – £230,000

About this role

Wood Mackenzie is the global leader in analytics, insights and proprietary data across the energy and natural resources landscape. The Vice President of Data Engineering defines, builds, and scales a modern, enterprise-wide data engineering capability within a federated operating model. This role leads design and delivery of robust, secure data pipelines using AWS-native architectures and Snowflake.

The VP establishes best-in-class engineering practices enabling domain-oriented data ownership while ensuring consistency through shared standards, governance, and platform capabilities. Responsibilities include architecting scalable data platforms with AWS services like S3, Glue, Lambda, EMR, Redshift, dbt, and Snowflake. Focus on enabling AI-ready data ecosystems with knowledge graphs, ontologies, and semantically enriched datasets.

Build, scale, and lead a high-performing data engineering organization including platform, enablement, and domain-aligned teams. Join a trusted team of 2,700 experts across 30 countries that breaks silos and connects industries, markets, and regions. Uphold Wood Mackenzie values: Inclusive, Trusting, Customer committed, Future Focused, Curious.

Empower customers to identify risk sooner, spot opportunities faster, and recalibrate strategy with confidence using Intelligence Connected and Synoptic AI. Drive advanced analytics, machine learning, and AI-native applications in a complex energy system. Accelerate change by fusing proprietary data with sharp analytical minds.

Requirements

Expertise in defining and scaling enterprise data engineering capabilities in federated models
Proven leadership in building and leading data engineering organizations
Deep knowledge of AWS-native architectures including S3, Glue, Lambda, EMR, Redshift
Proficiency with Snowflake-based data warehousing and dbt
Experience architecting secure, scalable data platforms
Strong understanding of data mesh principles, domain-oriented ownership, and governance
Familiarity with AI-ready data products like knowledge graphs, ontologies, and enriched datasets for ML/AI

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-in-class engineering practices, shared standards, governance, and platform capabilities
Enable development of AI-ready data ecosystems, including knowledge graphs, ontologies, and semantically enriched datasets
Lead design and delivery of robust, secure, and high-performing data pipelines
Ensure consistency across domains while promoting data ownership