Lead Data Engineer

As Lead Data Engineer, you will drive the design, build, and operation of production-grade data platforms and AI-enabled solutions in an enterprise context. You’ll act as a senior technical anchor, delivering end-to-end data and AI capabilities while guiding others through examples and best practices. The role spans legacy to cloud-native environments, focusing on reliable systems, governance, and scalable data pipelines. You will partner with cross-functional teams to operationalize AI, improve data sharing, and enable secure, scalable analytics and ML workloads. This is a hands-on, impact-focused leadership role that shapes how data and AI are used safely and effectively at scale.

• Design, build, and enhance enterprise data platforms, pipelines, and services
• Lead complex data engineering work end-to-end from problem definition to deployment and support
• Develop cloud-native data platforms supporting analytics, ML, and AI workloads
• Create data pipelines for traditional analytics and AI/ML use cases
• Collaborate with stakeholders to translate problems into scalable data and AI solutions
• Design data models, integrations, and storage for maintainability and reuse
• Establish AI-led development frameworks including CI/CD, testing, governance, and deployment standards
• Operationalize ML/AI solutions into production
• Improve data sharing and onboarding of new data sources across teams
• Diagnose and resolve complex data/platform issues in enterprise environments
• Contribute to an 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 skills with production data pipelines
• Deep understanding of the full data engineering lifecycle (ingestion, transformation, storage, serving, reuse)
• Experience designing integrations across diverse data sources and legacy environments
• Experience building and supporting cloud-based data platforms, preferably AWS
• Proven ability to design scalable data models and architectures
• Solid understanding of data governance, security, compliance, and operational controls
• Experience with modern engineering practices: source control, automated testing, CI/CD, infrastructure as code, deployment automation
• Ability to communicate with both technical and non-technical stakeholders
• Experience supporting ML/AI workloads through production-grade data engineering solutions

• Excellent communication with technical and non-technical stakeholders
• Delivery-focused mindset and collaboration
• Problem-solving mindset under complex enterprise conditions
• Python
• Spark
• AWS data services (S3, Glue, EMR, Redshift, Athena, Lambda, SageMaker, Bedrock)