At a Glance
- Tasks: Transform raw data into actionable insights and build analytics-ready data models.
- Company: Join Utility Warehouse, a leader in the energy sector with a focus on innovation.
- Benefits: Enjoy competitive pay, flexible working options, and opportunities for professional growth.
- Other info: Embrace a culture of collaboration and continuous learning while managing exciting projects.
- Why this job: Make a real impact by driving data-driven decisions in a dynamic environment.
- Qualifications: Proficient in SQL and data engineering, with strong analytical and communication skills.
The predicted salary is between 40000 - 50000 € per year.
Requirements
- Technical & Data Engineering Mastery
- Proven experience in writing optimised, modular SQL (Intermediate - advanced proficiency)
- Experience of building end-to-end, analytics-ready data models in Dataform, underpinned by a strong grasp of the engineering lifecycle (including Git/GitHub version control, merge conflict resolution, and automated unit testing)
- Analytics Architecture & Delivery
- Solid expertise in building reusable LookML code within Looker and the ability to independently lead medium-to-large analytical projects—from selecting the right tool for the job (e.g. Looker vs Mixpanel) to providing accurate estimations and hitting delivery timelines
- Commercial Impact & Domain Knowledge
- A track record of aligning analysis with business needs and translating complex datasets into actionable insights that influence stakeholder decisions
- Operational Excellence & Collaboration
- Experience in maintaining high data standards through observability (configuring freshness and quality alerts) and producing traceable documentation (Data Dictionaries), with a demonstrated ability to support non-technical teams through clear data storytelling
- UW skills and competencies
- Prioritisation
- Collaboration
- Communication
- Attention to Detail
- Project Management
What the job involves
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.
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.
Data Analyst (Energy) employer: Deepstreamtech
Utility Warehouse is an exceptional employer that fosters a collaborative and innovative work culture, where Data Analysts play a pivotal role in transforming data into strategic insights. With a strong emphasis on employee growth, we offer opportunities for professional development through hands-on experience with cutting-edge technologies and the chance to lead impactful projects. Located in a dynamic environment, our team thrives on diversity and inclusivity, ensuring that every voice is heard and valued.
StudySmarter Expert Advice🤫
We think this is how you could land Data Analyst (Energy)
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with potential colleagues on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data models, SQL queries, and any dashboards you've built. This gives you a chance to demonstrate your technical prowess and makes you stand out from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your data storytelling skills. Be ready to explain complex datasets in simple terms and how your insights can drive business decisions. Practice makes perfect!
✨Tip Number 4
Don't hesitate to apply through our website! Even if you don't tick every box, we want to hear from you. We believe in potential and are keen to see how you can contribute to our team.
We think you need these skills to ace Data Analyst (Energy)
Some tips for your application 🫡
Show Off Your SQL Skills:Make sure to highlight your experience with SQL in your application. We want to see your intermediate to advanced proficiency, so share examples of optimised, modular SQL you've written. This is your chance to shine!
Talk About Your Data Models:When you write about your experience, focus on any end-to-end data models you've built in Dataform. We love seeing how you've handled the engineering lifecycle, so mention your familiarity with Git/GitHub and any automated testing you've done.
Demonstrate Your Analytical Impact:We’re keen on candidates who can translate complex datasets into actionable insights. In your application, give us examples of how your analysis has influenced business decisions. Show us that you understand the commercial impact of your work!
Keep It Clear and Concise:Clarity is key! Make sure your application is well-structured and easy to read. Use bullet points where necessary and avoid jargon unless it’s relevant. Remember, we want to see your attention to detail, so proofread before hitting send!
How to prepare for a job interview at Deepstreamtech
✨Master Your SQL Skills
Make sure you brush up on your SQL skills before the interview. Be ready to discuss your experience with writing optimised, modular SQL and be prepared to showcase examples of your work. Practising common SQL queries can help you feel more confident when discussing your technical expertise.
✨Showcase Your Data Engineering Knowledge
Familiarise yourself with the engineering lifecycle, especially Git/GitHub version control and automated unit testing. Be ready to explain how you've applied these concepts in past projects. Highlight any experience you have with building end-to-end data models in Dataform, as this will demonstrate your ability to manage the deployment lifecycle effectively.
✨Communicate Your Insights
Prepare to discuss how you've translated complex datasets into actionable insights in previous roles. Think of specific examples where your analysis influenced stakeholder decisions. This will show that you not only understand the data but can also communicate its value clearly to non-technical teams.
✨Demonstrate Your Project Management Skills
Be ready to talk about how you've independently managed medium-to-large analytical projects. Discuss your approach to prioritisation, collaboration, and attention to detail. Providing examples of how you've met delivery timelines and handled stakeholder engagement will highlight your operational excellence.
