Enterprise Data Architect

As an Enterprise Data Architect for major commodities trader you will be the architectural ‘backbone’ and technical authority for a market leading portfolio of data / analytics products. This is very much a hands-on architect role: you will design enterprise-grade data solutions and guide their delivery and impact globally working with stakeholders ranging from engineering leads to energy traders.

You will be comfortable being the ‘go to’ and able to roll your sleeves up in any given situation.

The Role:

• Define the end-to-end data architecture across the enterprise, covering ingestion, storage, transformation, serving, and consumption layers.
• Make / document clear decisions on cloud vs. on-premise placement for data and analytics workloads, with explicit rationale and trade-off analysis.
• Design and optimise data warehouses and data models (relational, dimensional, and where appropriate, vault or lake-house patterns).
• Evaluate and recommend BI technologies (e.g. Power BI, Tableau, Qlik, in-house charting) on a use-case basis, defining when each is appropriate and when it is not.

Technical / Thought Leadership

• Act as a senior technical SME through the full project lifecycle: from discovery and design through to build, test, and production handover.
• Provide hands-on technical guidance during implementations: write proof-of-concept code, troubleshoot issues, and unblock delivery teams.
• Advise delivery teams on data architecture implications of design decisions, including feasibility, cost, performance, and maintainability.

Essential Qualifications

• Deep, demonstrable experience defining, developing, and evolving data architectures in complex, multi-domain organisations.
• Proven hands-on expertise in relational and dimensional data modelling, with the ability to justify modelling choices and explain trade-offs to others.
• Experience designing for both batch and real-time/streaming data patterns.
• Understanding of data mesh, data fabric, data warehouse and lake-house concepts and when they do (and don’t) apply.

Hands-On Technical Depth

• Working knowledge of SQL at an advanced level; comfortable writing, reviewing, and optimising complex queries and DDL.
• Practical experience with at least one major cloud data platform (Azure Synapse/Fabric, Snowflake, Databricks, BigQuery, AWS Redshift) including cost and performance tuning.
• Experience in energy trading, commodities, or financial services data environments (or Big Tech
• Familiarity with data governance tooling (e.g., Collibra, Purview, Alation).
• BSc in Computer Science, Information Management, or related discipline (or equivalent practical experience) from a Russell Group university
• Experience with DataOps or MLOps practices and how they intersect with data architecture.
• Prior experience working in a flat or lean organisational structure where the architect is also involved with the implementation.

• Deep, demonstrable experience defining, developing, and evolving data architectures in complex, multi-domain organisations.
• Proven hands-on expertise in relational and dimensional data modelling, with the ability to justify modelling choices and explain trade-offs to others.
• Experience designing for both batch and real-time/streaming data patterns.
• Understanding of data mesh, data fabric, data warehouse and lake-house concepts and when they do (and don’t) apply.

Hands-On Technical Depth

• Working knowledge of SQL at an advanced level; comfortable writing, reviewing, and optimising complex queries and DDL.
• Practical experience with at least one major cloud data platform (Azure Synapse/Fabric, Snowflake, Databricks, BigQuery, AWS Redshift) including cost and performance tuning.
• Experience in energy trading, commodities, or financial services data environments (or Big Tech
• Familiarity with data governance tooling (e.g., Collibra, Purview, Alation).
• BSc in Computer Science, Information Management, or related discipline (or equivalent practical experience) from a Russell Group university
• Experience with DataOps or MLOps practices and how they intersect with data architecture.
• Prior experience working in a flat or lean organisational structure where the architect is also involved with the implementation.