Lead Data Engineer (AI & Data Platforms) | Must Be DV Clearable | 5 Days Onsite

Hands-on technical leadership building data and AI systems that actually work

We're looking for a Lead Data Engineer to join a growing enterprise data and AI capability, playing a critical role in designing, building, and operating production-grade data platforms and AI-enabled solutions.

This is not a line-management role. Instead, it is a senior, hands-on technical leadership position for someone with deep data engineering experience who leads through delivery, technical judgement, and engineering excellence rather than hierarchy.

You will work across complex environments spanning legacy platforms, modern cloud services, advanced analytics, machine learning, and emerging AI technologies. The challenge isn't greenfield experimentation; it's building robust production systems, operationalising AI capabilities, improving what exists, and helping teams use data and AI safely, effectively, and at scale.

We're particularly interested in engineers who have helped organisations establish the foundations for AI-led development, including data platforms, ML pipelines, governance frameworks, deployment processes, and production support models.

What You'll Be Doing

You'll act as a senior technical anchor for both data engineering and AI-enablement initiatives, contributing directly while guiding others through example.

You will:

• Design, build, and enhance enterprise-scale data platforms, pipelines, and services
• Lead complex data engineering work end-to-end, from problem definition and exploration through build, testing, deployment, and operational support
• Build and maintain cloud-native data platforms that support analytics, machine learning, and AI workloads
• Develop robust data pipelines supporting both traditional analytics and AI/ML use cases
• Work directly with technical and non-technical stakeholders to translate operational problems into scalable data and AI solutions
• Design data models, integration patterns, and storage structures that support long-term maintainability and reusability
• Help establish engineering frameworks for AI-led development, including environments, CI/CD processes, testing strategies, governance controls, and deployment standards
• Support the operationalisation of machine learning and AI solutions into production environments
• Improve data sharing, onboarding of new data sources, and interoperability across teams and systems
• Evaluate and introduce new tools, frameworks, and technologies where they deliver genuine value
• Build robust, production-grade solutions rather than proofs of concept
• Treat metadata, lineage, observability, governance, data quality, and security as first-class engineering concerns
• Diagnose and resolve complex data and platform issues in challenging enterprise environments
• Contribute to a supportive engineering culture focused on quality, delivery, and continuous improvement

What We're Looking For

This role is for a genuine practitioner who enjoys doing the work and raising standards around them.

Essential Experience

• Significant hands-on experience as a Senior or Lead Data Engineer working on complex, enterprise-scale systems
• Strong Python and Spark skills with evidence of building and maintaining production data pipelines
• Deep understanding of the full data engineering lifecycle: ingestion, transformation, storage, serving, and reuse
• Strong experience designing integrations across diverse data sources, platforms, and legacy environments
• Experience building and supporting cloud-based data platforms, preferably within AWS environments
• Proven experience designing scalable data models and architectures
• Solid understanding of data governance, security, compliance, and operational controls
• Experience with modern engineering practices including source control, automated testing, CI/CD, infrastructure as code, and deployment automation
• Ability to communicate effectively with both highly technical and non-technical stakeholders
• Experience supporting machine learning or AI workloads through production-grade data engineering solutions

Highly Desirable

• Strong AWS data platform experience (S3, Glue, EMR, Redshift, Athena, Lambda, Bedrock, SageMaker or equivalent)
• Experience operationalising machine learning models into production environments
• Experience with MLOps, model deployment, model monitoring, model lifecycle management, and automated validation processes
• Experience supporting Generative AI solutions, including RAG architectures, vector search, LLM integration, AI orchestration, or agentic workflows
• Databricks experience, including Unity Catalog, MLflow, Delta Lake, Lakehouse architecture, and AI tooling
• Experience working within secure, regulated, or government environments
• Exposure to large-scale enterprise data and AI platforms

If you're passionate about building enterprise-scale data platforms, enabling AI adoption, and solving genuinely difficult engineering challenges, we'd love to hear from you. Apply now.