Staff Analytics Engineer

This is a fully remote position, open to applicants in United Kingdom.

📋 Description

• Establish and elevate the standards of analytics engineering. Create the patterns, testing protocols, and review practices that ensure dbt models across the organization are consistent, well-documented, and reliable without the need for personal oversight on each model.

• Take ownership of the context layer. Combine the semantic layer (Omni) with the foundational domain models into a single resource that can be confidently queried by the business, whether by humans or AI.

• Construct the domain model from the ground up. Transform the team's raw data into a structured, trustworthy layer that supports commercial, financial, risk, and operational decisions beyond mere dashboards.

• Broaden the influence of analytics engineering throughout the organization. Integrate new data sources, product and platform events, and tools utilized by other departments, extending analytics engineering from a centralized function into various areas of the company.

• Encourage broader thinking within the team. As the business explores significant opportunities like international expansion and innovative ways to offer its technology to other companies, assist in developing a domain layer prepared for these initiatives.

• Collaborate across the organization, not solely within the data team. Engage directly with engineers, product managers, and sales personnel to grasp their objectives, then translate those into models that perform effectively in real-world applications.

⛳️ Requirements

• You have experience building or redesigning a domain layer, have encountered failure states, and have learned what excellence looks like through practical experience.

• Extensive, production-level experience with dbt: custom macros, reusable patterns, and genuine work optimizing models that are costly to execute.

• Proficient in SQL and comfortable working on a modern data warehouse at scale (the team utilizes ClickHouse).

• Practical experience with a semantic layer or BI modeling tool (such as Omni, Looker, or similar), with real influence over metric definitions, not just their construction.

• A keen attention to detail and a strong QA discipline: you review your own work and prioritize accuracy in definitions without relying on others for oversight, all while keeping pace with a dynamic business environment.

• Background in commercial data, such as sales funnels or CRM pipelines, or in portfolio and financial trading data, including risk, hedging, forecasting, or time-series modeling.

• A proven record of implementing quality standards or tools that measurably improved a team's productivity, not just your own.

• Strong first-principles stakeholder management: you prefer to address challenging scoping questions upfront rather than risk building the wrong solution twice.

• Experience in the energy sector or another industry with significant physical or financial complexities underlying the data.

🏝️ Benefits

• Offers Equity