Engineering Data Integrity Lead
Grade: P3
Function: Engineering
Location: Gloucester
Security Clearance Required: Baseline BPSS and SC
Duration: Permanent
Hours: 37hrs per week
About the Role
The Engineering Data Manager is responsible for the governance, integrity, and lifecycle management of engineering data across product development and in‑service support activities. This role ensures that product design changes, technical changes, and configuration data are accurately controlled, authorised, and maintained within approved systems in accordance with internal procedures, customer requirements, and regulatory standards.
The position plays a critical role in safeguarding product and system data integrity during the exploitation of engineering data, establishing trusted data foundations that enable advanced analytics and the responsible adoption of Artificial Intelligence technologies, including Generative AI and Large Language Models.
Skills and Experience
* HNC/HND or Degree in Engineering, Engineering Management, or a related technical discipline.
* Proven experience in engineering data management, configuration control, or design change management.
* Strong understanding of product lifecycle management (PLM) concepts.
* Experience managing design and technical change in complex engineering environments.
* Demonstrated ability to maintain high levels of data integrity, accuracy, and traceability.
* Strong stakeholder management and communication skills.
* Ability to work effectively in regulated industries (e.g. aerospace, defence, automotive, rail, energy).
Desirable Skills & Experience
* Experience with PLM/PDM systems such as Windchill, Teamcenter, or Enovia.
* Knowledge of configuration management standards (e.g. ISO 10007, EIA‑649).
* Familiarity with quality and regulatory frameworks (e.g. AS9100, ISO 9001).
* Experience supporting audits, certifications, or customer data deliverables.
* Understanding of digital thread / digital twin concepts.
* Highly organised with strong attention to detail.
* Analytical and methodical problem‑solver.
* Confident in challenging non‑compliance and driving best practice.
* Comfortable working across multiple projects and prioritising effectively.
* Committed to continuous improvement and data excellence.
Responsibilities
Engineering Data & Integrity
* Own and maintain the integrity of engineering data infrastructure, defining strategy and leading the technical execution of change to ensure reliable data pipelines for analytics and business needs.
* Enable rapid improvements in Master Data quality to support effective data exploitation and the adoption of new and evolving systems.
* Ensure raw engineering data is accurately transformed into usable data assets through effective structuring, version control, and full traceability of product definitions, including drawings, models, specifications, and Bills of Material (BOMs).
* Support the exploitation of engineering data by ensuring data integrity (design pipelines that handle dirty data and flag discrepancies) underpins current and future business needs.
* Enable and support organisational AI initiatives by providing trusted data foundations and deploying advanced technologies and tools, including Generative AI and Large Language Models, to end users.
* Establish and enforce data standards, naming conventions, and classification rules across engineering datasets.
Product Design Change Management Integrity
* Strategy and Architecture - design, implement and manage the end-to-end data platforms which control product design change, ensuring all changes are correctly assessed, approved, implemented, and recorded.
* Pipeline and Infrastructure - drive the creation of scalable and efficient pipelines and processing systems for data ingestion, transformation and access - including engineering change requests (ECRs), engineering change proposals (ECPs), and change implementation activities.
* Ensure design changes maintain data compliance with safety, security (RBAC) secure data transfer protocols (sensitive data).
Data Governance and Quality through Technical Change Control
* Establish and enforce data management, security and compliance policies ensuring high data quality and integrity by administering formal technical change control processes, ensuring that technical impacts, dependencies, and risks are fully evaluated.
* Maintain accurate change histories and configuration baselines throughout the product lifecycle.
* Support configuration audits and design reviews by providing authoritative data sets.
Data Integrity & Assurance
* Ensure engineering data platforms remains accurate, complete, secure, fit for purpose and align with regulatory frameworks e.g., SOC2, ISO 27001, HIPAA.
* Perform regular data quality assessments and audits to identify discrepancies or integrity risks.
* Drive continuous improvement initiatives to reduce data errors, rework, and inefficiencies.
Cross-Functional Collaboration
* Act as the primary interface between data sciences, analytics, engineering, manufacturing, quality, supply chain, and programme teams on data-related matters.
* Provide expert advice on data governance, emerging techniques and technologies, drive adoption of new tools configuration status accounting, and promote DevOps.
* Support internal and external stakeholders, including customers and regulatory authorities.
Tools, Systems & Continuous Improvement
* Manage and optimise the use of tools (e.g. Apache Spark, AWS Glue, Databricks or equivalent).
* Support system enhancements of data processing and transformation, migrations, and process improvements.
* Contribute to the development of engineering data management policies, procedures, and training materials that support Data Lakehouses.
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