Technical Assurance Engineer - Data Migration & AI QA

Experis (Austria)

Shrewsbury

On-site

GBP 56,000 - 100,000

Full time

2 days ago

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Job summary

Experis (Austria) is seeking a SC-cleared Technical Assurance Engineer to support the DWIT programme in the UK, focusing on discovery of legacy data feeds and documentation for cloud-native migration.

You will establish QA standards, validate artefacts, and ensure traceability from source to target, collaborating with data engineers, SMEs and senior leads to meet project timelines and quality targets.

Qualifications

Proven experience defining and implemented technical QA strategies within large-scale data migration, modernisation or transformation programmes.
Strong understanding of data warehousing concepts, including ETL/ELT processing, SQL, data mappings, lineage, schema definitions, interfaces, and reconciliation.
Experience assuring technical documentation or specs against implementation evidence, with the ability to assess accuracy and completeness.
Practical experience using AI-assisted approaches for engineering, analysis or QA, with an understanding of the limitations of such approaches.
Experience defining measurable quality criteria, sampling approaches, and acceptance thresholds.
Experience with legacy ETL/ELT tooling such as Informatica PowerCenter, Pentaho Data Integration, Oracle PL/SQL, shell scripting, COBOL, or SAP Data Services would be advantageous.

Responsibilities

Establish quality standards, validation criteria and acceptance thresholds for all documentation artefacts, including mappings, transformations, lineage and specifications.
Implement quality assurance best practices for the legacy feeds in your team's scope, aligned to the overall project strategy.
Design and oversee QA processes to verify that outputs of the AI analysis pipeline accurately reflect legacy system behaviour, including AI-accelerated checks and manual reviews.
Assure the use of AI-enabled tooling and prompt engineering outputs, ensuring artefacts are consistent, accurate and contextually valid.
Validate completeness and traceability across artefacts, ensuring all relevant legacy inputs are accounted for and outputs link back to source code and logic.
Identify and manage quality risks, gaps and inconsistencies across the backlog, working with legacy service SMEs and your team to address gaps.
Collaborate with SMEs, data engineers and technical leads to define assurance approaches for complex or service-specific transformations and edge cases.
Oversee quality governance of output artefact repositories, ensuring adherence to structures, formats and version control practices.
Lead quality reviews, audits and walkthroughs with stakeholders prior to handoff to the feed migration teams.