Engineering Data Integrity Lead in Normanton

At Raytheon UK, we take immense pride in being a leader in defence and aerospace technology. We are dedicated to fuelling innovation, nurturing talent, and fostering a culture of excellence. Joining our team means being part of an organisation that shapes the future of national security whilst investing in your growth and personal development. We provide a collaborative environment, abundant opportunities for professional development, and a profound sense of purpose in what we do.

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 underpins current and future business needs.
• Establish and enforce data standards, naming conventions, and classification rules across engineering datasets.

• Product Design Change Management: 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.
• 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.
• Data Integrity & Assurance: Ensure engineering data platforms remain accurate, complete, secure, fit for purpose and align with regulatory frameworks.
• Cross-Functional Collaboration: Act as the primary interface between data sciences, analytics, engineering, manufacturing, quality, supply chain, and programme teams on data-related matters.
• Tools, Systems & Continuous Improvement: Manage and optimise the use of tools (e.g. Apache Spark, AWS Glue, Databricks or equivalent).