Senior Analytics Engineer position at Arbor Education.| United Kingdom

_{2026-04-24T13:04:31.829Z} bot

Arbor Education Senior Analytics Engineer

Experience: General

Pattern: Full-time

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High School (S.S.C.E)

United Kingdom

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Location: Remote Salary: £75,000 - £80,000 About us At Arbor, we’re on a mission to transform the way schools work for the better. We believe in a future of work in schools where being challenged doesn’t mean being burnt out and overworked. Where data guides progress without overwhelming staff. And where everyone working in a school is reminded why they got into education every day. Our MIS and school management tools are already making a difference in over 12,000 schools and trusts. Giving time and power back to staff, turning data into clear, actionable insights, and supporting happier working days. At the heart of our brand is a recognition that the challenges schools face today aren’t just about efficiency, outputs and productivity - but about creating happier working lives for the people who drive education everyday: the staff. We want to make schools more joyful places to work, as well as learn. About the role The Data function at Arbor powers the delivery of analytics and analytics-enablement to our customers, including schools, local authorities and multi-academy trusts. We are looking for a collaborative and experienced Senior Analytics Engineer to join our Data team and help us turn raw data into high-quality, actionable insights that power both our external customer products and our internal decision-making. The remit and focus of the role is to work at the intersection of data engineering and business logic, ensuring our Medallion-based Snowflake warehouse is robust, scalable, and optimised for our next phase of growth. It’s a broad and exciting role, so we’re looking for someone up for a challenge - if you’re highly technical and a proactive problem-solver, this is the role for you. Core responsibilities Data Modelling & Transformation: Lead the development and maintenance of our dbt-enabled data models, transforming raw data into Silver and Gold layers within our Snowflake Medallion architecture. Product Delivery: Build and optimise the data structures that power our customer-facing BI products, ensuring high performance and accuracy for our external users. AI Integration: Architect and deliver high-quality data sets specifically designed to be consumed by both core applications, AI features and LLM-driven functionalities within the Arbor suite. AI-First Workflow: Embrace and pioneer the use of AI-assisted engineering tools to accelerate planning, architecting, and coding tasks, setting the standard for efficiency within the team. Collaboration: Work closely with Warehouse Data Engineers to ensure upstream data quality. Domain Exploration: Partner with Product Managers and domain experts to translate business requirements into technical schemas, bridging the product teams into the data function. Data Integrity: Support the wider data team in maintaining rigorous data quality and observability standards, utilising Datadog and dbt tests to ensure one version of the truth. Mentorship & Best Practice: Act as a Senior pillar for the analytics engineering community, performing code reviews and championing best practices. About you Data Warehouse & dbt Expert: Extensive experience building complex, performant data models within a modern warehouse (Snowflake, Databricks etc.) ideally using dbt. You should have a deep understanding of Jinja, macros, and dbt project structure. SQL & Python: Mastery of SQL for analytics use cases is a must. You should also be comfortable using basic Python for data manipulation tasks that sit outside the standard SQL transformation layer. Medallion Architecture: Proven experience working structured warehouse/lakehouse architectures, ideally within a Bronze/Silver/Gold framework. Product Thinking: Experience building data products for external and internal customers, with an understanding of how to balance performance with deep analytical utility. AI-First Mindset: A proactive interest in how AI is changing data engineering. You should be able to demonstrate how you use AI tools (like Cursor, CoCo or LLMs) to improve your output and how you prepare data for AI consumption. Stakeholder Management: Ability to bridge the gap between technical data engineering and the needs of Product Managers and internal domain experts. Ownership Culture: Demonstrable experience working in an environment where quality, performance, and observability are core responsibilities of the engineer. Bonus Skills Power BI: Familiarity with Power BI (or Looker, etc.) for front-end visualisation and an understanding of how to optimise the data layer for BI consumption. DataOps: Experience with CI/CD pipelines for data (e.g., GitHub Actions) and automated testing frameworks.

Senior Analytics Engineer job opportunity at Arbor Education.

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