Data Analyst

We're on the hunt for talented Data Analysts to join the team as we grow our business.

Our vision is simple, an effortlessly rewarding experience where customers only need to tell us something once. We are redesigning our digital landscape to ensure that being multi-service means being seamless, whether that’s a lightning-fast onboarding journey, automated home moves, or proactive push notifications. We are investing heavily on solving root causes, not just symptoms. We are looking for product-minded talent to own these journeys, turning complex utility data into simple, transparent, and high-value digital experiences that customers enjoy using.

We deliver progress. What you’ll do and how you will make an impact.

We work in small, fully autonomous teams that have real ownership of their products. We use the best tool for the job and constantly look for better.

As a Data Analyst at Utility Warehouse, you will own the complete "Data-to-Insight" value chain for single projects. This role combines the technical skills to build production-grade data pipelines with the soft skills to manage stakeholders and drive domain-specific decisions. 

You are the core "builder" of the team. This role marks the transition from implementing to designing. The defining characteristic of this level is the independent management of the deployment lifecycle. You will be responsible for ensuring that data transformation logic is not just correct, but reproducible, tested, and version-controlled. 

You will not just report on data; you will build and maintain the analytics layer data models and automated pipelines that power our company’s decision-making engine. You will leverage modern data stack technologies (SQL, BigQuery, Dataform, Looker, Mixpanel) to transform raw data into high-value strategic assets, ensuring scalability and accuracy across business functions.

Your responsibilities will include:

• Analytics Engineering (Dataform/dbt): Build and maintain end-to-end analytics-ready data models in Dataform, independently handling complex logic and transformations.
• CI/CD & DevOps (Data version control): Apply version control (through tools like Git & Github) and unit testing practices to analytics models, to ensure data assets are stable, reproducible, and ready for deployment.
• Performance Engineering: Write optimised SQL, proactively tuning for performance (cost & runtime) and addressing alerts about data quality.
• Ownership: Independently plan and execute medium-to-large requests through to completion; identify and proactively address tech debt.
• Actionable Insight: Translate complex data into actionable business insights. Move beyond "what happened" to explain "why it happened" and recommend "what to do next."
• Dashboards/Reporting: Build visual self-serve dashboards and reports for users to access KPIs / metrics and explore/analyse pre-built data set.
• Stakeholder Management: Manage the full request lifecycle. Actively engage with stakeholders to refine broad requests into clear requirements, providing accurate estimations and delivering within agreed timelines.
• Domain Ownership: Develop deep knowledge of a specific domain (e.g., Marketing, Product, Finance). Understand the impact of key metrics on the business and participate in defining team goals based on this knowledge.