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# Data Platform Engineer

- **Company:** Amplius
- **Location:** Milton Keynes, UK
- **Salary:** £47,000.0 - £51,000.0
- **Contract:** Temporary contract
- **Skills:** Clean Code Principles, Amazon Web Services, Microsoft Azure, Big Data, Code Review, Databases, Continuous Integration, Data as a Services, Data Validation, Data Governance, Data Infrastructure, Extract Transform Load (ETL), Data Structures, Python (Programming Language), Metadata, Operational Data Store, Scala (Programming Language), Software Engineering, SQL Databases, Azure Data Factory, Git, Information Technology, Data Lineage, Data Management, Azure Synapse Analytics, Data Pipelines, Databricks, Programming Languages
- **Published:** September 26, 2026
- **Apply:** http://www.mydiversity.com/job/data-platform-engineer-at-amplius-milton_keynes/14118

## About the Role

* A degree or equivalent experience in Computer Science, Data/Software Engineering or a similar subject, with a strong foundation in core database systems and data structures.
 * Proficiency in Databricks and big data coding languages, including SQL, Python and Scala.
 * Experience designing and deploying ETL/ELT pipelines using Azure Data Factory, Azure Synapse Analytics or equivalent tools in AWS/GCP.
 * Strong engineering skills, with the ability to write clean, maintainable code, and test and document your work effectively.
 * Strong communication and collaboration skills, with the ability to explain technical trade-offs clearly and work effectively across teams and departments.
 * Right to work in the UK is an essential requirement - we do not provide sponsorship.

## Description

Are you experienced in building reliable data platforms? Join Amplius as a Data Platform Engineer and help us to build secure, scalable data pipelines that turn operational data into trusted datasets for reporting, analytics and responsible AI.

Your week: 36.25 hours - Monday to Friday 9am to 5.15pm, * Design, build and operate secure, reliable and cost-effective data pipelines using Azure services and Databricks.

 * Ingest and transform data from different sources, handling changes in data and schemas effectively.
 * Improve pipeline performance, reliability and cost, while automating tasks and maintaining clear technical documentation.
 * Build data quality checks, validation and reconciliation, and contribute to metadata, data lineage and governance requirements.
 * Use Git, code reviews, CI/CD and Infrastructure-as-Code to deliver safe, repeatable changes, supported by effective monitoring, runbooks and deployment evidence.
 * Work closely with Data Product Engineers, BI & Reporting, IT Solution Architects, Data Governance, Cyber and DPO teams to deliver secure, well-governed data services and support priority datasets.


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