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Future Interest: Analytics Engineer

Kaluza
CompanyKaluza
CategoryEngineering
LocationBristol
RemoteOn-site (inferred)
EmploymentNot stated
LevelNot stated
SalaryNot stated by the employer
Posted31 Mar 2026
Last verified30 Jul 2026
SourceEmployer career page (greenhouse)
Applications are handled by the employer, not by us.Apply on the employer's site →
Description
Job title:  Future Interest: Analytics Engineer  Location: London, Bristol or Edinburgh (Hybrid working model) Salary: £46,400 - £63,800 Team: Analytics Engineering team Reporting To: Analytics Engineering Manager IMPORTANT Note on this role: While we don’t have an immediate opening for this specific position today, we are growing fast and hire for this skillset frequently! By applying here, you are joining our Priority Talent Pool and when a headcount is approved, our recruiters check this pool before posting the job publicly. This role will be based in London, Bristol or Edinburgh and requires an existing right to work in the UK. At this time, we are not able to offer visa sponsorship for this role. We are committed to building a diverse, global team and our sponsorship policy is evaluated on a role-by-role basis. We encourage you to keep an eye on our careers site to stay informed about future opportunities where we are able to offer visa sponsorship. Kaluza is the Energy Intelligence Platform, turning energy complexity into seamless coordination. We help energy companies overcome today’s challenges while accelerating the shift to a clean, electrified future. Our platform orchestrates millions of real-time decisions across homes, devices, markets and grids. By combining predictive algorithms with human-centred design, Kaluza makes clean energy dependable, affordable and adaptive to everyday life. With teams across Europe, North America, Asia and Australia, and a joint venture with Mitsubishi Corporation in Japan, we power leading companies including OVO, AGL and ENGIE, as well as innovators like Volvo and Volkswagen. What will I be doing? As an Analytics Engineer, you will play a pivotal role in architecting the foundation of our global reporting capabilities. Your primary focus over the coming year will be the design and implementation of a high-performance, scalable dimensional model built to serve multiple global clients. This role is not just about moving data; it is about the sophisticated translation of complex schema designs into production-ready SQL. You will be the bridge between abstract data architecture and the high-performance scripts that power our business intelligence. Key Responsibilities Dimensional Model Architecture: Lead the transition to a robust, multi-tenant dimensional model. You will be responsible for ensuring the architecture is scalable enough to onboard several major global clients without compromising performance. Expert SQL Development: Translate complex schema designs into high-quality, performant SQL scripts. You will own the logic that transforms raw, disparate data into structured clean tables. Global Scalability: Design and optimise data structures that handle high volume and variety, ensuring that our data infrastructure evolves ahead of our expanding international client base. Stakeholder Translation: Act as a key technical partner for business stakeholders. You must be able to navigate complex requirements from various departments and translate them into technical specifications and elegant data models. Data Integrity & Performance: Rigorously test and optimise SQL queries and data transformations to ensure "single source of truth" reliability and lightning-fast query response times for end-users. Candidate Profile Must-Haves Advanced SQL Mastery: You are an expert in writing, tuning, and debugging complex SQL. You understand window functions, CTEs, and query execution plans inside and out. Stakeholder Management: Proven ability to communicate technical concepts to non-technical audiences and manage competing priorities from multiple business units. Engineering Mindset: A focus on writing clean, modular, and reusable code. Nice-to-Haves Data Modelling Expertise: Practical experience with Star Schema and Kimball methodologies. Modern Data Stack: Experience using Databricks for process
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