In this role you will design, build and validate data migration pipelines to move data from legacy systems to AWS-based Lakehouse architectures. You work closely with architects, engineers and analysts to deliver scalable migration solutions, ensuring data quality, integrity and performance. You will leverage ETL/ELT tooling and cloud data platforms to support secure, compliant migrations and continuous improvement of pipelines. This is a hands-on, delivery‑focused position in a collaborative Data & AI practice with a strong emphasis on testing and validation.
Pay / Benefits
Tailored benefits
Learning and development opportunities
Flexible work options
Equal opportunities employer
Disability Confident Committed Employer
Responsibilities
Support delivery within data migration programmes and key workstreams
Collaborate with architects, engineers and stakeholders to implement migration solutions
Plan and execute data migration tasks and deliverables
Build and maintain data migration pipelines from legacy warehouses to AWS platforms
Develop ETL/ELT pipelines using AWS Glue, Python/PySpark, SQL, YAML configurations
Support bulk and incremental data migrations and pipeline repointing to cloud environments
Test ETL/ELT pipelines on AWS services (AWS Glue, Apache Iceberg) and validate migrations to Data Lakehouse architectures
Execute and validate initial bulk loads and incremental data processing
Write and run SQL queries to verify data completeness, accuracy and transformation outputs
Support development of test scripts and validation checks
Work with AWS services (Glue, S3) and Iceberg to improve pipeline performance and reliability
Apply transformation logic based on data mapping rules and prepare data for target-state models
Collaborate with Solution Architects, Data Engineers, Migration Architects, Analysts and QA teams
Follow established engineering standards and contribute to documentation and reusable components
Support data quality, governance and security during migration and assist with compliance activities
Key requirements
Experience in data engineering or data migration delivery
Strong focus on testing, validation, and data quality assurance
Ability to work across data pipelines and transformation workflows
Good analytical and problem-solving skills
Effective communication and teamwork skills
Willingness to learn and develop in data migration and cloud technologies
Hands-on experience with AWS cloud services, especially AWS Glue
Python / PySpark
SQL querying and validation
YAML configuration (desirable)
Experience testing or supporting ETL/ELT pipelines and data migration processes
Familiarity with data lake / Lakehouse concepts (Apache Iceberg)
Distributed processing frameworks (Spark)
Basic understanding of ETL vs ELT approaches and cloud-based data architectures
Exposure to version control and CI/CD tools (desirable)
We use essential cookies to make this site work. With your permission we would also like to use the optional cookies below. Nothing optional is set until you choose, and you can change your mind at any time from "Cookie settings" at the bottom of every page. Cookie policy.
Essential — keep the site working and secure, and remember the choice you make here. Always on.