Technical Assurance Engineer

eTeam Workforce Limited; United Kingdom; Remote; GBP 83,000 - 112,000; Full time

Job summary

eTeam Workforce Limited is seeking a Technical Assurance Engineer for a 6-month assignment in the United Kingdom. You will ensure that the discovery outputs for the DWIT programme are complete, accurate and traceable, enabling migration teams to rebuild legacy feeds as cloud-native equivalents.

You will collaborate with data engineers, architects and SMEs, defining QA standards, overseeing AI-assisted analysis, and guiding assurance across artefacts, mappings, schema definitions and metadata to

Qualifications

• Experience defining and implementing QA strategies in large-scale data migration projects.
• Strong data warehousing and ETL/ELT knowledge.
• Ability to challenge discovery findings for completeness.
• Experience with AI-assisted engineering, analysis or QA.
• Experience with legacy ETL/ELT tooling would be advantageous.

Responsibilities

• Establish quality standards, validation criteria and acceptance thresholds for artefacts.
• Implement QA practices aligned to project strategy.
• Design and oversee QA processes for AI analysis outputs.
• Assure AI tooling and prompt engineering outputs are accurate.
• Validate completeness and traceability of artefacts.
• Identify and manage quality risks and gaps.
• Collaborate with SMEs and data leads to define assurance approaches.
• Oversee quality governance of artefact repositories.
• Lead quality reviews and audits with stakeholders.

Skills

Technical QA strategies

Data migration

Data warehousing concepts

ETL/ELT knowledge

AI-assisted QA

Quality governance

Tools

Informatica PowerCenter

Pentaho Data Integration

SQL

Shell scripting

COBOL

Job description

We are a Global Recruitment specialist that provides support to the clients across EMEA, APAC, US and Canada. We have an excellent job opportunity for you.

Job Title: Technical Assurance Engineer

Location: United Kingdom

Duration: 6 months

Pay Rate: £529 per day, all-inclusive (PAYE through Umbrella)

Clearance Required: SC is required.

Job Description:

• Supporting Data Warehousing and Integration Transformation (DWIT) programme, we are supporting the discovery of hundreds of Legacy data feeds across diverse technical stacks, with complex interdependencies.
• Analysing and documenting the Legacy solutions, using AI assistance with human and agentic assurance, is the critical first step to enabling migration teams to successfully transform these services into cloud-native solutions.
• Assurance of the discovery output is critical to successful completion not just of discovery, but the migration programme as a whole.
• Legacy services are formed of various technologies, including SQL, PL/SQL, bash scripts, Informatica Power Centre, Pentaho Data Integration, COBOL, SAP Data Services, and others.
• Discovery will be supported by several teams of data engineers, architects, business analysts, and assurance leads, working through a backlog of 1000+ feeds to document all functionality.

This role

• Your core responsibility will be to establish whether the discovery output provides a complete, accurate, and traceable representation of the Legacy data solution, sufficient for migration teams to rebuild the feed accurately.
• Outputs will include design documentation, interface catalogues, schema definitions, business and technical metadata, source-to-target mappings, markdown (or equivalent) code specifications, and structured knowledge captured within custom RAG-backed AI solutions.
• With the volume of feeds to transform, the programme's desired pace of delivery, and modern engineering practices, use of AI will be central to the team's ways of working.
• Since these systems underpin key business processes, assuring that the new systems give equivalent outputs to the existing ones is a core acceptance criteria.
• Discovery and Legacy support teams will also support comparison testing of the new and old systems to validate this.

Working closely with your discovery team, you will:

• 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 both AI-accelerated checks and manual review approaches
• Assure the use of AI-enabled tooling and prompt engineering outputs, ensuring that generated artefacts are consistent, accurate and contextually valid
• Validate completeness and traceability across artefacts, ensuring that all relevant Legacy inputs are accounted for and that outputs can be clearly linked 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 agreed structures, formats and version control practices
• Lead quality reviews, audits and walkthroughs with stakeholders, ensuring confidence in outputs prior to handoff to the feed migration teams

Skills required

• Proven experience defining and implementing technical assurance or QA strategies within large-scale data migration, modernisation or transformation programmes.
• Strong understanding of data engineering and data warehousing concepts, including ETL/ELT processing, SQL, data mappings, lineage, schema definitions, interfaces, and reconciliation.?
• You will need sufficient technical depth to challenge discovery findings and identify where documented behaviour is wrong or incomplete.
• 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, Client PL/SQL, Shell scripting, COBOL, or SAP Data Services would be advantageous.