London, England, United Kingdom
Actively hiring, first seen by us todayWork setup in the employer’s posting: On-siteFrom the employer’s posting
Employment type in the employer’s posting: Full-timeFrom the employer’s posting
Own the target architecture and roadmap for the Google Cloud lakehouse platform
Define the shared enterprise data model across energy, broadband, mobile and insurance
Prototype reference architectures and write SQL and Python proofs of concept
Design semantic layers and secure access patterns for GenAI and AI agents
Own the target architecture and evolutionary roadmap on Google Cloud
Define a shared business vocabulary and conformed entities across Energy, Broadband, Mobile, and Insurance
Prototype reference architectures, write SQL and Python, build proofs of concept
Design the semantic layers, rich metadata, retrieval interfaces, and secure agent access patterns
Actively hiringFirst seen by us today, still listed, and not a repost of an earlier posting.
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- 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).
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 the target architecture and evolutionary roadmap on Google Cloud, applying open lakehouse principles to store data once, avoid vendor lock-in, and serve many analytical and operational engines without duplicate copies.
- 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).
- 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.
- 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.
We deliver progress. What you’ll do and how you will make an impact. 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.
- 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 the target architecture and evolutionary roadmap on Google Cloud, applying open lakehouse principles to store data once, avoid vendor lock-in, and serve many analytical and operational engines without duplicate copies. - Build the core enterprise data model: Define a shared business vocabulary and conformed entities across Energy, Broadband, Mobile, and Insurance, choosing fit-for-purpose modelling patterns (Data Vault, wide analytical tables, operational entities, or ontologies).
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New Data Leadership postings at the employers we follow are up 7% over the last 5 weeks compared with the 5 before.
Each bar is one week of new postings. The darkest bars are the last 5 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
We file this job under Data Leadership, by the work the posting describes.
Data leaders run data teams or design how a company's data fits together. Managers hire, set priorities and answer for whether the data work pays off; architects choose the systems and the shape of the data.
Expect years of hands-on data work, experience leading people or projects, and the ability to explain tradeoffs to executives.
Unlike a senior engineer or analyst, most of a data leader's week goes to people, planning and decisions rather than building.
The government has no code for data leaders, so these figures are for the closest group, computer and information systems managers, which includes data processing and data operations managers.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 11-3021.
Not sure you meet all the requirements? Let us decide! Research shows that women and members of other underrepresented groups tend not to apply for jobs if they think they may not meet every qualification, when in fact they often do.
We provide equal opportunities, a diverse and inclusive work environment, and fairness for everyone. You are welcome to apply no matter your age, disability, gender, marriage or civil partnership status, pregnancy and maternity status, race, religion or belief, or sexual orientation. Please don’t be afraid to ask about what we can do to support your needs. All requests will be carefully and fairly considered.
Please note, if you are successful and offered a role at UW, you will be subject to a background check. Where checks are unsatisfactory or incomplete and/or a failure to reveal information relating to convictions that you are required to identify as part of the background checks, could lead to withdrawal of an offer of employment.
Hi! We're UW.
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.
The challenge
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.
Got your attention? Read on..
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.
We put people first. It’s all about you..
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.
Here is the technical experience and toolkit you will bring to the table:
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.
We work together. Your team and the people you will work with...
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.
We deliver progress. What you’ll do and how you will make an impact.
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 the target architecture and evolutionary roadmap on Google Cloud, applying open lakehouse principles to store data once, avoid vendor lock-in, and serve many analytical and operational engines without duplicate copies.
Build the core enterprise data model: Define a shared business vocabulary and conformed entities across Energy, Broadband, Mobile, and Insurance, choosing fit-for-purpose modelling patterns (Data Vault, wide analytical tables, operational entities, or ontologies).
Architect AI-ready & agentic data foundations: Design the semantic layers, rich metadata, retrieval interfaces, and secure agent access patterns that allow GenAI models and AI agents to discover and safely interact with UW data.
Embed data governance by design: Collaborate with the Data Governance Lead to build automated data quality checks, lineage tracking, access controls, masking, and data contracts into the pipeline infrastructure from day one.
Establish architecture governance & delivery standards: Lead lightweight architecture decision records (ADRs), create "Golden Path" templates, and agree clean integration contracts and schema evolution standards with engineering teams.
So why pick UW?
We’ve got big ambitions so there’s going to be plenty of challenges. There are also a lot of benefits:
Apply here!
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We welcome applications from diverse and varied backgrounds so get your application in and let’s chat!
Nicola Dorrington will be your point of contact throughout the recruitment process.
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London, England, United Kingdom
- Holiday: 25 days plus bank holidays (increasing with tenure), with the option to trade up to five days each year. - Hybrid working, with at least 2 days in our Farringdon office. - Electric Car salary sacrifice scheme through Tusker
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