Data Engineer

Data Freelance Hub; Greater London; Hybrid; GBP 60,000 - GBP 80,000; Full time

Job summary

A data solutions company is looking for a Data Engineer for a 6-month hybrid contract in London. You'll focus on building data ingestion and transformation pipelines, implementing data cleansing, and working with geospatial data. The role requires strong SQL and Python skills, experience with modern data tools like Spark, and solid knowledge of data profiling and quality checks. Join a dynamic team focused on improving data processes within the Breast Screening Pathway team.

Qualifications

• Strong experience in data transformation-focused pipelines.
• Advanced SQL knowledge for profiling, joins/merges.
• Experience with Python data wrangling packages.
• Proven experience with geospatial data workflows.
• Ability to build robust data quality checks.

Responsibilities

• Build secure, repeatable data ingestion and transformation pipelines.
• Implement data cleansing rules and produce auditable outputs.
• Analyse existing system datasets to identify data issues.
• Develop data transformation-heavy pipelines.
• Utilise advanced SQL for profiling and performance tuning.

Skills

SQL

Python

Data Profiling

Geospatial Data

Data Cleansing

Data Transformation

Azure Data Factory

Spark

Tools

Pandas

Polars

Scikit-learn

Matplotlib

Job description

Overview

⭐ - Featured Role | Apply direct with Data Freelance Hub

This role is for a Data Engineer with a 6-month contract, offering an inside IR35 pay rate. Located in London/Remote, it requires expertise in SQL, Python, geospatial data, and modern data tools, focusing on data transformation within the Breast Screening Pathway team.

Location: London/Remote, United Kingdom

Role Details

Contract: 6 months initially

IR35: Inside IR35

Work arrangement: Hybrid

Responsibilities

• Build secure, repeatable data ingestion and transformation pipelines for the Breast Screening Pathway team.
• Implement data cleansing rules and produce auditable, reproducible outputs.
• Analyse existing system datasets spread across multiple system instances to identify data issues and shape target datasets and structures for the future.
• Establish import/export patterns, handle data extracts, schema discovery, incremental loads, and work with multiple source instances.
• Develop data transformation-heavy pipelines from data profiling to cleansing to standardisation, conformance, and publishing.
• Utilise advanced SQL for profiling, joins/merges, deduplication, anomaly detection, and performance tuning.
• Write maintainable Python code for automation, parsing, rules engines, and data quality checks; work with data wrangling packages (Pandas, Polars) and visualization tools (Matplotlib).
• Experience with modern data tooling (e.g., Spark, Azure Data Factory) or equivalent implementations.
• Work with geospatial data (formats such as GeoJSON, shapefiles; CRS; spatial analysis workflows) and apply geographical context in data processing pipelines.
• Work with publicly available official datasets such as ONS open data products (census boundaries, lookups, indices, population estimates).
• Build rules for completeness/validity/consistency and implement exception handling.

Qualifications

• Strong experience in data transformation-focused pipelines, data profiling, cleansing, standardisation, conformance, and publishing.
• Advanced SQL knowledge for profiling, joins/merges, deduplication, anomaly detection, and performance tuning.
• Python scripting for automation, parsing, rules engines, and data quality checks; maintainable code.
• Experience with Python data wrangling packages (e.g., Pandas, Polars), modelling (e.g., scikit-learn), and visualization (e.g., matplotlib).
• Experience with modern data tooling (e.g., Spark, Azure Data Factory) or equivalents.
• Proven experience with geospatial data and related workflows.
• Ability to translate local/regional geospatial insights into wider national/regional datasets and outputs.
• Experience with official datasets such as ONS open data products.
• Ability to build robust data quality and completeness checks, with error handling.

Note: This description retains the core role expectations and requirements as provided, with formatting improvements for readability and compliance with formatting standards.