Wood Mackenzie is the global data and analytics business for the renewables, energy, and natural resources industries. Enhanced by technology. Enriched by human intelligence. In an ever-changing world, companies and governments need reliable and actionable insight to lead the transition to a sustainable future. That’s why we cover the entire supply chain with unparalleled breadth and depth, backed by over 50 years’ experience. Our team of over 2,400 experts, operating across 30 global locations, are enabling customers’ decisions through real-time analytics, consultancy, events and thought leadership. Together, we deliver the insight they need to separate risk from opportunity and make confident decisions when it matters most.
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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 Summary
We are seeking an experienced Data Architect to lead, design and manage our modern data infrastructure, with a strong focus on Snowflake, data modelling, dbt, and data engineering. The ideal candidate will play a key role in shaping AI-centric data solutions that support scalable product development, analytics, operational efficiency, and strategic decision-making.
With an emphasis on Data as a Product, this role requires strong leadership, effective cross-functional communication, and a strategic mindset. In collaboration with the Head of Data, the incumbent will develop a robust operating model for implementing the data strategy, ensuring that required skills and capabilities are established throughout the organisation. Additionally, they will be responsible for ensuring data contracts are established, guaranteeing that all data is suitably prepared for product development, analytics, and AI applications.
Role Responsibilities
Design and implement robust data architectures leveraging Snowflake and AWS cloud data platform.
Develop and maintain scalable data models to support product delivery, analytics and business intelligence needs.
Lead the design and implementation of data transformation workflows using dbt or other identified solutions
Establish operating model, with data/analytical engineers to build reliable data pipelines, models and integrations.
Ensure data quality, governance, and security across all data systems and platforms in collaboration with broader architecture group.
Optimize data storage, performance, and cost within the cloud data environment.
Document architectural standards, best practices, and data lineage.
Provide technical leadership and mentorship on data engineering, modelling and architecture topics.
Experience of using AI to accelerate all aspects of good data management.
Key Skills and Experience
Proven experience designing and managing data architectures using Snowflake and AWS environments at scale.
Excellent leadership, collaboration and communication skills across large, multi-disciplined organisations.
Practical expertise in designing enterprise ontologies and knowledge networks.
Strong understanding of dimensional and normalized data modelling techniques.
Hands-on expertise in dbt for data transformation and pipeline orchestration.
Solid background in data engineering with proficiency in SQL and Python.
Experience with ELT/ETL frameworks and modern data stack tools.
Knowledge of data governance, access control, and privacy best practices.
Proficiency in applying AI foundations to ensure data is aligned for consumption and content generation
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.
