> Markdown version of [/jobs/ext/4666391-data-engineer](https://www.wearedevelopers.com/jobs/ext/4666391-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:** Proactive.IT Appointments Ltd. - **Location:** Exeter, United Kingdom (Remote available) - **Salary:** £104,000 - £117,000 - **Contract:** Temporary contract - **Skills:** Amazon Web Services, Microsoft Azure, Customer Data Management, Extract Transform Load (ETL), Data Warehousing, Apache Hive, Python (Programming Language), Cloud Services, Standard Sql, SQL Databases, Transact-SQL, Cloud Platform System, Git, Pandas, Microsoft Fabric, Pyspark, Software Version Control, Data Pipelines - **Published:** November 11, 2026 - **Apply:** https://www.reed.co.uk/jobs/data-engineer/57558264 ## About the Role * Experience building and supporting data pipelines in a cloud data platform, ideally Microsoft Fabric. * Strong Python, including pandas and PySpark. * Strong SQL, including Spark SQL or T-SQL. * Experience cleaning and standardising data * Experience matching, deduplicating or reconciling records across systems, whether rule-based or probabilistic. * Understanding of how to measure data quality and match accuracy, including false positives and missed matches. * Good working knowledge of UK GDPR and handling personal data securely. * Use of version control (Git) and writing readable, documented, tested code. * Ability to explain technical decisions and trade-offs clearly to non-technical colleagues., * Amazon Web Services (AWS) * Cloud Services & Infrastructure * Communication Skills * Data Pipeline Development * Data Warehousing * ETL * Microsoft Azure * Python Programming * SQL Databases * Stakeholder Management ## Description Our client is looking for an experienced Data Engineer with strong Microsoft Fabric, PySpark and Splink experience. You'll be responsible for developing data matching pipelines, cleansing and standardising customer data, tuning matching models and building accurate master customer records across multiple systems.