Lead Data Engineer

You will lead the design and delivery of scalable data platforms that enable high-quality analytics and data-driven products. In this role, you’ll set standards for reliability, governance, and engineering excellence while partnering with stakeholders to deliver trusted datasets. You’ll own data modelling and performance optimization, shape data products, and guide a team of engineers. This is a chance to influence data architecture at scale within a cross-functional engineering context.

• Architect and deliver modern data platforms (lakehouse/warehouse) and ETL/ELT pipelines
• Set engineering standards for code quality, testing, security, and operations
• Own data modelling approaches (dimensional, Data Vault, domain-oriented) and optimize cost/performance
• Implement orchestration, monitoring, alerting; manage SLAs and incident response
• Drive data governance: lineage, catalog, access controls, retention policies, GDPR-aligned practices
• Collaborate with analytics, software and platform teams to deliver reusable data products and APIs
• Mentor engineers, conduct code reviews, and contribute to hiring and capability development

• Significant data engineering experience with leadership of teams or major initiatives
• Expert SQL and strong Python and/or Scala
• Cloud data engineering experience (AWS, Azure or GCP) including object storage and managed services
• Experience with modern data warehousing/lakehouse tools (Snowflake, BigQuery, Redshift, Databricks)
• Hands-on orchestration experience (Airflow, Dagster, Prefect) and CI/CD for data pipelines
• Experience with streaming/event-driven data (Kafka, Kinesis, Pub/Sub) is highly desirable
• Proficiency with infrastructure as code (Terraform, CloudFormation) and containerisation (Docker; Kubernetes beneficial)
• Strong understanding of data quality, testing (unit/integration), and observability
• Excellent stakeholder management and communication skills

• stakeholder management
• communication
• leadership
• SQL
• Python
• Scala