Lead Data Engineer

In this role you will lead hands-on the design, build, and operation of production-grade data platforms and AI-enabled solutions within an enterprise. You will act as a technical anchor across data engineering and AI initiatives, guiding delivery and engineering excellence while collaborating with cross-functional teams. The role spans legacy and cloud environments, aiming to operationalise AI capabilities and scale data-driven decision making. You’ll help establish foundations for AI-led development, including governance, deployment, and production support. This is a senior, individual-contributor leadership position focused on delivering robust systems and real impact.

• Design, build, and enhance enterprise-scale data platforms, pipelines, and services
• Lead end-to-end data engineering work from problem definition to deployment and support
• Build and maintain cloud-native data platforms supporting analytics, ML, and AI workloads
• Develop robust data pipelines for traditional analytics and AI/ML use cases
• Collaborate with technical and non-technical stakeholders to translate problems into scalable data/AI solutions
• Design data models, integration patterns, and storage structures for long-term maintainability
• Establish engineering frameworks for AI-led development including environments, CI/CD, testing, governance, and deployment standards
• Operationalise machine learning/AI solutions into production environments
• Improve data sharing, onboarding of sources, and interoperability across teams
• Evaluate and adopt new tools/technologies that add genuine value
• Deliver production-grade solutions and treat metadata, lineage, observability, governance, data quality, and security as core concerns
• Diagnose and resolve complex data/platform issues in enterprise environments
• Contribute to a supportive engineering culture focused on quality and continuous improvement

• Significant hands-on experience as a Senior or Lead Data Engineer on complex, enterprise-scale systems
• Strong Python and Spark with production data pipelines
• Understanding of the full data engineering lifecycle (ingestion, transformation, storage, serving, reuse)
• Experience designing integrations across diverse data sources, platforms, and legacy environments
• Experience building and supporting cloud-based data platforms, preferably AWS
• 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, IaC, deployment automation
• Ability to communicate with both technical and non-technical stakeholders
• Experience supporting ML/AI workloads through production-grade data engineering solutions

• strong communication
• collaboration
• problem-solving
• Python
• Spark
• AWS data services (S3, Glue, EMR, Redshift, Athena, Lambda, SageMaker, etc.)