Data Engineer

Data Engineer – Inside IR35 – Remote A strong Data Engineer is required to work on a Data Transformation workstream within a complex, end-to-end service delivery environment.
The role supports a programme focused on modernising and transforming a large-scale national service through secure, scalable, and data-driven digital capabilities.
Working as part of a small team of data specialists, you will design and build robust data ingestion and transformation pipelines across multiple source systems.
You will play a key role in identifying and resolving data quality issues, shaping future target datasets and structures, and producing auditable, reproducible outputs.
The core focus of this role is to build secure, repeatable data pipelines, implement data cleansing and standardisation rules, and ensure high-quality, traceable data products suitable for operational and analytical use.
Essential Skills Experience Data Engineering Pipelines Establish import/export patterns, including handling data extracts, schema discovery, incremental loads, and multiple source system instances Build transformation-heavy pipelines covering data profiling, cleansing, standardisation, conformance, and publishing Advanced SQL skills for data profiling, joins and merges, deduplication, anomaly detection, and performance tuning Strong scripting capability in Python for automation, parsing, rules engines, and data quality checks, with an emphasis on writing maintainable, well-structured code Experience using Python libraries for data wrangling (e.g. Pandas, Polars), modelling (e.g. scikit-learn), and visualisation (e.g. matplotlib) Experience with modern data tooling (e.g. Spark, Azure Data Factory) or the ability to implement equivalent capabilities through code Data Quality, Governance Traceability Build and implement rules for data completeness, validity, and consistency Design exception handling and data reconciliation workflows Develop versioned pipelines with deterministic transformations, logging, lineage, and traceability of changes Produce auditable and reproducible datasets suitable for downstream consumption Geospatial Public Data Proven experience working with geospatial data, including spatial formats (e.g. vector, raster, GeoJSON, shapefiles), coordinate reference systems, and spatial analysis workflows Strong ability to interpret and apply geographical context within data processing pipelines Demonstrated capability to aggregate, upscale, or translate local or regional geospatial insights into broader national or regional-level datasets and analytical outputs Experience working with publicly available official datasets, particularly Office for National Statistics (ONS) open data products (e.g. census boundaries, geographic lookups, deprivation indices, population estimates) Security Environment Comfortable working in secure environments, applying least-privilege access principles Experience handling sensitive datasets, including secure storage, secure