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Data Migration Engineer
Job in
London, Greater London, W1B, England, UK
Listed on 2026-09-09
Listing for:
NTT DATA
Full Time
position Listed on 2026-09-09
Job specializations:
-
IT/Tech
Data Engineering, AWS
Job Description & How to Apply Below
We are seeking a detail-oriented and capable Data Migration Engineer to join our Data & AI practice. The successful candidate will bring solid experience in data migration, ETL/ELT pipeline development, and cloud-based data platforms, with a focus on AWS Data Lakehouse environments.
This role is key to supporting the design, build, and validation of data migration pipelines, enabling the successful transition of data from legacy systems to modern cloud platforms. You will contribute to ensuring data quality, integrity, and performance, particularly through structured testing and validation activities.
You will work closely with architects, senior engineers, and analysts to deliver scalable and reliable migration solutions, using technologies such as AWS Glue, Apache Iceberg, Python/PySpark, SQL, and YAML configurations. You should be comfortable working in a collaborative, delivery-focused environment and have a strong interest in data migration, cloud technologies, and modern data engineering practices.
What you'll be doing:
Client Engagement & Delivery Support delivery within data migration programmes, contributing to key work streams
Collaborate with architects, engineers, and stakeholders to implement migration solutions
Assist in planning and executing data migration tasks and deliverables
Data Migration Engineering Build and maintain data migration pipelines from legacy data warehouses to AWS-based platforms
Develop ETL/ELT pipelines using:
AWS Glue Python / PySparkSQLYAML configurations
Support execution of bulk data migrations and incremental/delta loads
Assist with pipeline repointing and migration to cloud environments
Data Pipeline Testing & Validation (Core Focus)
Test ETL/ELT data pipelines on AWS services, including AWS Glue and Apache Iceberg Support validation of data pipeline migrations to AWS Data Lakehouse architectures
Test pipelines using:
Python/PySpark transformations SQL-based validation logicYAML-driven configurations
Execute and validate:
Initial bulk data loads
Incremental/delta data processing
Write and run SQL queries to validate:
Data completeness
Data accuracy
Transformation outputs
Support development of test scripts and validation checksAWS Data Platforms & Lakehouse Work with AWS services including:
AWS GlueS3-based data lakes
Support implementation of Data Lakehouse architectures, including Apache Iceberg Contribute to improving pipeline performance and reliability
Data Transformation & Support Apply transformation logic based on defined data mapping rules
Support preparation of data for target-state models
Assist in ensuring consistency between source and target datasets
Collaboration & Best Practices Work collaboratively with:
Solution Architects Data Engineers Data Migration Architects Analysts and QA teams
Follow established engineering standards and best practices
Contribute to documentation and reusable components
Quality, Governance & Security Support maintenance of data quality and integrity during migration
Follow secure data handling practices
Assist with compliance requirements, including:
GDPRPublic sector data standards (where applicable)
Contribute to testing, validation, and audit activities
What experience you'll bring:
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
Technical Expertise Hands-on experience with:
AWS cloud services, especially AWS Glue Python / PySparkSQL querying and validationYAML configuration (desirable)
Experience testing or supporting:
ETL/ELT pipelines
Data migration processes
Familiarity with:
Data lake / Lakehouse concepts (e.g., Apache Iceberg)
Distributed processing frameworks (e.g., Spark)
Basic understanding of:
ETL vs ELT approaches
Cloud-based data architectures
Exposure to version control and CI/CD tools desirable
Who we are:
We’re a business with a global reach that empowers local teams, and we undertake hugely exciting work that is…
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