SC Technical Assurance Engineer CGEMJP00363660

Experis (Austria); Shrewsbury; On-site; GBP 56,000 - 100,000; Full time

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.

Skills

Tech assurance

QA strategies

Data warehousing

ETL/ELT

SQL

Data mappings

Schema definitions

AI QA

Prompt engineering

Quality governance

Backlog mgmt

Stakeholder teamwork

Legacy tooling

Tools

Informatica PowerCenter

Pentaho Data Integration

Oracle PL/SQL

Shell scripting

COBOL

SAP Data Services

Job description

SC Technical Assurance Engineer CGEMJP00363660

Role Title: Technical Assurance Engineer

Duration: 6 month contract

Clearance required: Active SC Clearance held with a governing body

Supporting the client's 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 implemented 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, Oracle PL/SQL, shell scripting, COBOL, or SAP Data Services would be advantageous.

All profiles will be reviewed against the required skills and experience. Due to the high number of applications we will only be able to respond to successful applicants in the first instance. We thank you for your interest and the time taken to apply!