Data Platform Senior Engineer (SEL & SWL) x2

Who are we looking for?

We are looking for an experienced Data Platform Engineer who can design, build, test and maintain modern data pipelines and analytical data models.You will understand the data lifecycle from ingestion and orchestration through transformation, modelling, validation, deployment, monitoring and optimisation. You will be comfortable investigating problems, improving existing solutions and working with senior engineers to apply appropriate engineering standards.Your experience is likely to include several of the following:Microsoft Fabric Snowflake Azure OneLake Lakehouse architecture Data Factory Spark Python SQL ETL/ELT dimensional modelling Data Vault orchestration Git Azure DevOps/GitHub CI/CD Infrastructure as Code data quality MLOps performance optimisationYou will be pragmatic and collaborative, able to understand the problem before developing the solution and communicate technical concepts clearly to both technical and non-technical colleagues.Above all, we are interested not simply in what you have built, but what became better because you built it.

What makes the role different?

For us, successful Data Engineering is not simply about moving data from one place to another. We want our engineers to understand how the data they build and maintain is ultimately used.You will work closely with the analysts and Data Scientists who consume the data, giving you the opportunity to connect engineering decisions directly to analytical delivery.You will help us move away from unnecessary manual processing and isolated solutions towards automated, reusable, governed and production-quality data products. The difference you make should be visible: reducing processing time, eliminating repetitive analyst intervention, improving data quality and pipeline reliability, increasing reuse, improving performance or shortening the time needed to develop analytical products.The role therefore provides an opportunity to develop deep technical expertise while contributing directly to measurable analytical value .

What will you do?

Design, build and optimise scalable data pipelines develop secure and reliable data flows from source systems through ingestion and transformation to curated analytical data products.

Develop data orchestration, transformation and integration solutions build and maintain automated ETL/ELT processes covering ingestion, cleansing, transformation, scheduling, dependency management and exception handling.

Build robust analytical data models translate analytical and business requirements into reusable dimensional, Lakehouse, Data Vault and semantic models.

Automate manual and repetitive processes identify opportunities to reduce unnecessary intervention and improve the productivity of Data Engineering and analytical teams.

Apply modern engineering disciplines use Git-based version control, CI/CD, automated testing, monitoring, documentation, deployment pipelines, change control and Infrastructure as Code.

Engineer for data quality, reliability and resilience implement validation, reconciliation, monitoring, audit, recovery, backfill and exception-handling processes.

Develop and optimise solutions across Microsoft Fabric, Snowflake and Azure contribute to performance, scalability, resilience and efficient resource utilisation.

Support advanced Analytics, Data Science and AI develop the pipelines, data structures and engineering components required for analytical and Machine Learning solutions.

Work collaboratively with analysts, Data Scientists and stakeholders understand downstream requirements and develop data products that solve genuine analytical and organisational problems.

Demonstrate the benefit of your engineering contribute to measurable improvements in processing time, manual intervention, reliability, data quality, reuse and analytical delivery.

Why join us?

You will work within an integrated Intelligence & Insights function supporting South East and South West London, alongside Data Platform Engineers, Data Scientists, Population Health specialists and wider analytical teams working with complex health and care data.Your engineering will directly support Population Health Management, strategic commissioning, planning, performance and wider analytical delivery. You will be able to see how your technical work translates into practical analytical capability and better decision support.You will also be joining during an important period of development, with the opportunity to work across Microsoft Fabric, Snowflake and Azure and to contribute to the evolution of modern Data Engineering practices across the function.If you are an experienced Data Platform Engineer who combines strong technical capability, curiosity, collaboration and a determination to turn good engineering into measurable analytical value , we would like to hear from you.

Design, build and optimise scalable data pipelines develop secure and reliable data flows from source systems through ingestion and transformation to curated analytical data products.

Develop data orchestration, transformation and integration solutions build and maintain automated ETL/ELT processes covering ingestion, cleansing, transformation, scheduling, dependency management and exception handling.

Build robust analytical data models translate analytical and business requirements into reusable dimensional, Lakehouse, Data Vault and semantic models.

Automate manual and repetitive processes identify opportunities to reduce unnecessary intervention and improve the productivity of Data Engineering and analytical teams.

Apply modern engineering disciplines use Git-based version control, CI/CD, automated testing, monitoring, documentation, deployment pipelines, change control and Infrastructure as Code.

Engineer for data quality, reliability and resilience implement validation, reconciliation, monitoring, audit, recovery, backfill and exception-handling processes.

Develop and optimise solutions across Microsoft Fabric, Snowflake and Azure contribute to performance, scalability, resilience and efficient resource utilisation

Support advanced Analytics, Data Science and AI develop the pipelines, data structures and engineering components required for analytical and Machine Learning solutions.

Work collaboratively with analysts, Data Scientists and stakeholders understand downstream requirements and develop data products that solve genuine analytical and organisational problems.

Demonstrate the benefit of your engineering contribute to measurable improvements in processing time, manual intervention, reliability, data quality, reuse and analytical delivery.

Please see attached job description for full details of the main responsbilities.