SC Cleared Lead Data Engineer (AI Buildout)

The Role:

Working as a Lead Data Engineer supporting the client's enterprise AI buildout.

This role is responsible for leading the design and implementation of complex data flows, pipelines and integrations to enable advanced AI and automated workflow solutions.

Requirements:

• Develop and construct data products and services and integrate them into systems and business processes.
• Design and lead the implementation of data flows to connect operational systems, data for analytics and Business Intelligence (BI) systems.
• Establish data integration standards and lead the engineering of automated data pipelines, ETL/ELT workflows and API connections connecting enterprise SaaS and internal databases to AI applications whilst ensuring alignment with existing pipelines and standards.
• Recognise opportunities to re-use existing data flows
• Lead the build of data streaming systems
• Optimise the code to ensure processes perform optimally
• Lead work on database management
• Implement identity and access controls, including SSO, conduct data quality, sensitivity and access checks before indexing and ensure AI applications respect source system permissions.
• Act as the key bridge between technical engineering partners and business leads, interpreting data requirements and translating complex data risks into clear choices for stakeholders.
• Build teams to complete data integration services.
• Select and implement the appropriate technologies to deliver resilient, scalable and future-proofed data solutions.
• Respond to problems in databases, data processes, data products and services as they occur.
• Use agreed standards and tools to design, code, test, correct and document moderate-to-complex programs and scripts from agreed specifications and subsequent iterations

Skills Required:

• Hands on expertise in building scalable ETL/ELT pipelines, designing robust API integrations for large-scale enterprise AI architectures.
• Expert knowledge of Identity and Access Management, data classifications standards and UK GDPR/DPA compliance frameworks within AI environments.
• Experience conducting data quality assessments, structuring synthetic testing environments, and evaluating dataset readiness for complex automation or ML models.
• Advanced proficiency in AWS, Python, SQL and query optimisation.
• Demonstrated experience implementing CI/CD pipelines using Terraform, automated testing and metadata management best practices.
• Proven ability to translate complex technical data risks into strategic guidance for senior leadership.
• Track record of leading cross-functional engineering teams in agile environments.