VP Data Engineering

Wood Mackenzie

VP of Data Engineering overseeing scalable AWS-native data platforms and enhancing AI-ready ecosystems at Wood Mackenzie. Leading a high-performing data engineering organization with a global footprint.

Posted 4/30/2026full-timeEdinburgh • 🇬🇧 United KingdomLeadWebsite

Tech Stack

Tools & technologies

AirflowAmazon RedshiftAWSCloudSpark

About the role

Key responsibilities & impact

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

Requirements

What you’ll need

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.

Benefits

Comp & perks

Equal opportunities employer
Flexible working opportunities

ATS Keywords

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Hard Skills & Tools

data engineeringdata ingestiondata transformationdata orchestrationdata governancedata modelingperformance optimizationcost managementdistributed data processingstreaming technologies

Soft Skills

leadershipstakeholder managementcommunicationinfluenceorganizational change