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Utility Warehouse Limited in the United Kingdom seeks a Data Architect to lead a greenfield Enterprise Data Warehouse and migrate data to a dimensional modelling environment. You will bridge business needs with technical strategy, architect scalable data platforms on Google Cloud, and guide governance, security, and SDLC practices across teams.
You will prototype reference architectures, write SQL and Python, and collaborate with Data Platform Engineering, Data Science, and Data Governance to
We're on a mission to take the headache out of utilities by providing them all in one place. One bill for energy, broadband, mobile and insurance and a whole lot of savings! We're aiming to double in size as we help more people to stop wasting time and money. Big ambitions, to be delivered by people like you.
For our customers and Partners, UW just needs to work - there when you need it, and invisible when you don't. Just like flicking a switch.
Our proposition to customers is simple, but for our technology teams, the behind-the-scenes complexity is what makes it so interesting. Learn more about life in our Tech teams here.
We are seeking a Data Architect to lead a greenfield initiative: building the backbone of our new Enterprise Data Warehouse and migrating our data estate to a dimensional modelling environment.
You are a builder and architect who is as comfortable writing code and prototyping as you are mapping target architecture. You see how data connects across customer journeys, billing, smart metering, and Partner commissions. You stay pragmatic about delivery, hold clear technical standards, and communicate trade-offs simply and effectively across teams.
Enterprise platform expertise: Experienced across data engineering, data platforms, or data architecture, and will have acted as a lead data or solution architect designing end-to-end modern data platforms or lakehouses.
Modern lakehouse mindset: Deep understanding of open lakehouse design and open table formats (such as Apache Iceberg or Delta Lake), keeping data open and portable.
Practical, multi-paradigm data modelling: Deep, hands-on experience designing data models across Data Vault 2.0, dimensional modelling, entity-centric models, and domain-driven design, with the judgement to choose the right pattern for each job.
Hands-on coding skills: Strong hands-on coding and query capabilities in SQL and Python, building working proofs of concept and reviewing production code.
Cloud & streaming foundations: Proven experience with modern cloud data warehouses - ideally Google Cloud (BigQuery, BigLake, Dataplex, Dataproc, Vertex AI) - and designing for operational pipelines and change-data-capture (CDC) using streaming tech (such as Pub/Sub, Dataflow, or Kafka).
Governance and SDLC engineering: Practical experience building data governance, metadata lineage, quality checks, access control, and privacy directly into platform architecture, supported by CI/CD, Git workflows, and Infrastructure as Code (Terraform).
AI-ready architecture (desirable): Experience preparing data infrastructure for GenAI and agentic AI - including semantic layers, retrieval (RAG), vector search, knowledge graphs, or Model Context Protocol.
You will join our Technology & Product function within the Data family, reporting directly to the VP of Data and AI.
Data sits right at the heart of our FTSE 250 business, powering everything from half-hourly settlement and billing to customer insight and agentic AI. You will work closely with Data Platform Engineering, Data Science, and source-system teams to turn architecture blueprints into living, breathing production platforms. You will also pair up with our Data Governance Lead and DataSecOps to embed data contracts, data quality checks, and privacy-by-design standards that meet the high bar of regulated markets (Ofgem, Ofcom, FCA), ensuring everyone can build faster with complete confidence.
You will own the end-to-end data architecture for UW's single enterprise data platform on Google Cloud, shaping the foundation that powers day-to-day operations, analytics, and autonomous AI agents:
Provide hands-on technical leadership: Prototype reference architectures, write SQL and Python, build proofs of concept, model core entities yourself, and pair with engineers on complex design and code reviews.
Design the enterprise lakehouse platform: Own