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# Head of Data Platform

- **Company:** EDFT
- **Location:** London, UK
- **Experience:** Expert
- **Salary:** £86,115.0
- **Contract:** Permanent contract
- **Skills:** Training Data, Application Programming Interfaces (APIs), Artificial Intelligence, Data Analysis, Cloud Computing, Cyber Security, Data Architecture, Data Cleansing, Information Engineering, Data Governance, Data Infrastructure, Data Integrity, Data Warehousing, Identity and Access Management, Metadata, Meta-Data Management, Performance Tuning, Reference Data, Cloud Services, SQL Databases, Data Streaming, Enterprise Data Management, Data Strategy, Core Data, Storage Technologies, Enterprise Integration, Apache Kafka, Data Management, Machine Learning Operations, Programming Languages
- **Published:** October 22, 2026
- **Apply:** https://www.adzuna.co.uk/jobs/details/5936466244

## About the Role

* Substantial leadership experience across enterprise data, data engineering, data governance, analytics or data product management in a complex organisation.
 * Demonstrable delivery of enterprise-scale data platforms and operating models, ideally in energy trading, commodities or financial services.
 * Experience managing critical market, trade, reference, risk, finance or regulatory data with demanding quality, timeliness and control requirements.
 * Track record of building multidisciplinary teams and leading material change across technology and business functions.
 * Experience of vendor and commercial management for data platforms, cloud services and market data providers.
 * Strong executive-level communication, investment governance and benefits management experience.

Technical Requirements

 * Deep understanding of modern data architecture, including event streaming, Kafka, APIs, batch ingestion, lakehouse patterns, data warehousing and real-time distribution.
 * Strong knowledge of data engineering practices using SQL and relevant programming languages, storage design, performance optimisation, testing, deployment and observability.
 * Practical expertise in metadata management, lineage, data quality, master and reference data, ownership models, access governance and control assurance
 * Understanding of analytics, reporting, machine learning and AI lifecycles, including data preparation, feature provenance, monitoring and governance.
 * Knowledge of cloud and hybrid architecture, cybersecurity, privacy, resilience, cost management and enterprise integration., * Enterprise thinker who treats data as a product and a controlled asset, not simply a technology platform.
 * Able to challenge local optimisation and drive reuse, standardisation and clear ownership across organisational boundaries.
 * Commercially aware, evidence-led and comfortable making investment and prioritisation decisions.
 * Strong communicator who can translate between executives, data specialists, engineers and control functions.
 * Collaborative across business domains and able to create shared ownership of data outcomes.
 * Inclusive leader who develops talent and builds a culture of quality and accountability.

## Description

Own EDF Trading's enterprise data strategy and the capabilities required to provide trusted, governed and readily usable data. The role leads data product management, market data systems, core data engineering, plus data governance and quality. You will be accountable for turning data into a managed enterprise asset while enabling analytics, advanced modelling and AI at scale., * Define and execute the enterprise data strategy, target architecture, operating model and multi-year roadmap in alignment with business and technology strategy.

 * Lead data product management, market data systems, core data engineering, and data governance and quality as an integrated enterprise capability.
 * Establish accountable ownership for critical data domains, datasets and data products, with clear quality requirements, service expectations and lifecycle plans.
 * Prioritise investment and delivery across ingestion, streaming, APIs, warehousing, reporting and advanced analytics capabilities.
 * Ensure metadata, lineage, data quality controls, access controls, retention, privacy and control evidence are embedded into platforms and delivery practices.
 * Build strong partnerships across the trading, operations, risk, finance and corporate functions to convert business needs into reusable data products rather than one-off pipelines.
 * Own the market data capability, including distribution, entitlements, vendor strategy, usage transparency, cost management and resilience.
 * Provide governed enablement for data science and AI, including trusted training data, reproducibility, model lifecycle dependencies and responsible-use controls.
 * Set measures for data reliability, quality, adoption, delivery flow, platform performance, cost and value realisation.
 * Develop senior leaders and specialist teams, balancing central standards with federated domain accountability and avoiding duplicated data engineering.