> Markdown version of [/jobs/ext/4000077-data-engineer](https://www.wearedevelopers.com/jobs/ext/4000077-data-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer - **Company:** Robert Walters - **Location:** Nottingham, UK - **Experience:** Expert - **Salary:** £50,000.0 - £60,000.0 - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Application Programming Interfaces (APIs), Amazon Web Services, Microsoft Azure, Computer Programming, Databases, Data Validation, Information Engineering, Extract Transform Load (ETL), Data Retrieval, Data Warehousing, Database Design, Python (Programming Language), Cloud Services, SQL Databases, Azure Data Factory, Data Lakes, Data Pipelines, Databricks - **Published:** October 29, 2026 - **Apply:** https://www.reed.co.uk/jobs/data-engineer/57504220 ## About the Role * Proven experience working as a Data Engineer within a complex organisational setting. * Good exposure to SQL, Python and Databricks. * Proficiency in Azure Data Factory, Azure Logic Apps, Databricks, or APIs for integration. * Understanding of Data Lakes, Synapse Analytics, or Delta Lakes. * Advanced programming skills in languages like Python, Java or Scala enabling efficient automation of processes. * Strong knowledge of database design principles including modelling techniques for warehousing and visualisation tools., * Amazon Web Services (AWS) * Cloud Services & Infrastructure * Communication Skills (Key) * Data Engineering Roles * Data Pipeline Development * Data Warehousing * ETL (Extract Transform Load) * Microsoft Azure (Line) * Python Programming (Beginner) * SQL Databases ## Description * Developing data pipelines that ingest, process, and transform information from multiple sources. * Implementing seamless integration processes to ensure continuous flow of accurate information. * Designing, optimising, and managing databases to facilitate efficient data retrieval and storage. * Creating efficient ETL processes that transform raw data into usable formats. * Building and maintaining structured data warehouses that support easy retrieval, reporting, and analysis. * Ensuring strict data quality checks and validation procedures to proactively monitor accuracy and rectify any integrity issues.