Lead Data Engineer

Job requirements

• Degree-level education in a technology-focused subject such as Computer Science, Information Technology, or equivalent experience through professional qualifications and industry background.

• Strong academic or professional track record in a relevant technical discipline.

• 10+ years of experience in data-related roles, with the majority in data engineering.

• Strong hands-on experience with Snowflake, ideally 3+ years.

• Strong hands-on experience with dbt, ideally 3+ years.

• Experience leading small data engineering teams, ideally 2–3 engineers.

• Experience designing, building, and maintaining data processing systems at scale.

• Experience with Azure cloud services, Airflow, dbt, Kubernetes, GitHub, and Terraform.

• Experience working in fast-moving, high-growth, or startup-like environments is preferred.

• Strong communication skills with the ability to explain data warehouse plans and technical decisions to internal clients and stakeholders.

• A curious, delivery-focused mindset with a strong commitment to quality, innovation, and continuous improvement.

• Hands-on problem-solving ability and a practical approach to getting things working reliably.

• Contributions to open-source software or an active GitHub profile would be an advantage.

Job responsibilities

• Lead a small team of data engineers responsible for a specific business domain within the data platform.

• Own the design, development, and ongoing maintenance of scalable data pipelines that support new and existing data products.

• Work closely with internal stakeholders to understand data requirements, translate needs into technical solutions, and communicate delivery plans clearly.

• Ensure data quality, consistency, and reliability through the use of modern testing, validation, and monitoring tools.

• Design and implement new pipelines using best-in-class tools such as Airflow, dbt, and Snowflake.

• Contribute to the development of scalable cloud infrastructure on Azure, including infrastructure as code using Terraform.

• Promote engineering best practices, including clean code, maintainability, testing, and strong documentation.

• Support continuous improvement across the team by identifying opportunities to improve delivery, reliability, and operational efficiency.

• Stay current with emerging data engineering tools, techniques, and approaches, bringing fresh ideas and a continuous improvement mindset.