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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Data Engineer - **Company:** IMP Software - **Location:** Exeter, UK (Remote available) - **Experience:** Expert - **Salary:** £70,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Software as a Service, Databases, Continuous Integration, Information Engineering, Data Governance, Data Infrastructure, Extract Transform Load (ETL), Data Retrieval, Data Warehousing, Financial Software, Machine Learning, Microsoft SQL Server, Systems Development Life Cycle, Power BI, Standard Sql, Software Engineering, SQL Databases, Large Language Models, Snowflake, Git, Pyspark, Software Version Control, Data Pipelines, Databricks - **Published:** September 21, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=4a34611b6b5afd2e ## About the Role * Hands-on experience designing, building, and running data pipelines into a cloud warehouse or lakehouse in production. * Experience with a modern cloud data platform: Snowflake, Databricks, or similar. * Strong SQL and data modelling: dimensional modelling and slowly changing dimensions. * Solid experience with a modern transformation framework such as dbt (ideal), Matillion, or PySpark. * Experience extracting data from source databases, with bonus points for multi-tenant SaaS data or single-tenant to centralised migrations. * Strong judgment on data quality, and the ability to own technical decisions and explain the trade-offs. * Solid software engineering practices: version control with Git, CI/CD, and a disciplined software development lifecycle (SDLC). * Comfortable as a senior IC, setting standards and mentoring as the team grows, without needing formal line management to do it. Nice to have * Python for data engineering: pipelines, tooling, and automation. * AI-assisted engineering: comfortable using AI coding agents (e.g. Claude Code) to build and ship faster. * Some data science grounding (statistical or machine-learning methods) relevant to benchmarking and analytics. * AI/ML data patterns (LLM-ready data, text-to-SQL, RAG), and semantic / metrics layers (dbt metrics, Cube.dev, or similar). * Data governance, GDPR, or anonymisation; SQL Server background; experience in a SaaS or product company. * Typically 5+ years in data engineering, with a track record of owning production pipelines and data models end to end. Depth of craft and judgment matter more than an exact number of years. ## Description IMP builds the finance software that Multi-Academy Trusts use to plan budgets, manage their money, and run procurement across their schools. Since 2019 we've grown to around 550 trusts and 6,000 schools, roughly half the MAT market, with a 4.9/5 customer rating and fresh investment behind us. We hold one of the richest datasets on MAT finance in the sector, and we're building a data platform to unlock it: cross-trust benchmarking, AI-driven querying, and a new wave of analytics. You'll join right at the start as our first Data Engineer, building that platform hands-on alongside the Head of Data Engineering. You'll own the design and delivery of major components, set the technical patterns and quality bar early, and grow into mentoring as the team expands. The work you ship in the first year will be the foundation everything else is built on. The Work Working within the architecture set by the Head of Data Engineering, you'll: * Help design and stand up the data warehouse / lakehouse, and build the pipelines that ingest data from our source systems into it. * Build the centralised data model and the dbt transformation layer, version-controlled, tested, and documented. * Build data quality validation, monitoring, and alerting, and implement row-level security for multi-tenant data. * Build and maintain the semantic / metrics layer: one governed definition of each metric, in parity with our agreed source of truth (ASOT), feeding Power BI, in-product reporting, and the AI layer alike. * Build the data foundations for our AI and intelligence layer, so "chat with your data" and proactively pushed insights are accurate and safe to surface to trusts inside the product. ## Related Videos - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) - [Beyond Dashboards: Fixing Text-to-SQL with Semantic RAG](https://www.wearedevelopers.com/videos/2036-beyond-dashboards-fixing-text-to-sql-with-semantic-rag) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) ## Related Articles - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)