Manager, IT - Data Engineering

Job description

• Lead and manage a team of data engineers, providing technical guidance and mentorship to ensure their growth and development
• Take design leadership over individual projects — own architecture decisions end-to-end, not just contribute
• Take charge of day-to-day team activities including scrum ceremonies, sprint planning, and backlog management
• Oversee the development and operation of modern data engineering solutions, including data ingestion, processing, integration, and governance
• Collaborate with stakeholders to identify business needs and develop solutions that meet those needs
• Bachelor's degree or higher in Computer Science, Statistics, Business, Information Technology, or a related field
• 5+ years of experience in data engineering or a related discipline, with at least
• 2 years in a technical lead or team lead capacity
• Proven track record delivering and supporting software and data engineering capabilities in a fast-paced, dynamic environment Experience contributing to shared frameworks within the data domain, and creating data assets used in mission-critical applications Intermediate data design skills — data modelling, schema design, and pipeline architecture for enterprise-scale solutions Core Python proficiency — including object-oriented programming, reusable library design, and clean scalable code; not just ad hoc scripting Advanced SQL — window functions, query optimisation, and complex transformation logic beyond basic CRUD operations; experience with dbt is a plus Intermediate DevOps and cloud (Azure, AWS, or GCP) — including CI/CD pipeline ownership, deployment practices, and cloud cost awareness Experience with Agile methodologies; hands-on experience running scrum ceremonies Experience with automated testing and data testing frameworks — able to design and enforce test coverage across pipelines and data assets
• 5+ years building enterprise data solutions with a proven track record delivering high-quality pipelines and analytics products
• Familiarity with modern orchestration and transformation tooling — Dagster, dbt, and Snowflake experience strongly preferred Understanding of data warehousing concepts and architecture patterns such as medallion architecture and dimensional modelling Awareness of data product concepts including lineage, certification, and operational telemetry Experience implementing data quality frameworks — including validation, profiling, and monitoring — to ensure integrity and consistency across data systems Experience working in environments where data engineering capabilities are shared as platform services, not built in isolation
• Strong collaborator and team player, with the ability to work effectively with business stakeholders and cross-functional teams
• Strong analytical thinker — able to troubleshoot complex pipeline issues, optimise performance, and identify improvement opportunities across data systems Clear point of view on data engineering best practices — and the ability to bring others along, not just hold the opinion Effective communicator who can translate technical decisions into business language
• Develop and maintain shared frameworks for data engineering; contributions should reflect design for broader organisational scope, not just the immediate team Contribute to the onboarding of new data engineers, analysts, product owners, and other team members joining the project
• Contribute to community-of-practice enablement programs — including internal upskilling and certification, training frameworks, and knowledge-sharing initiatives
• Ensure platform stability and operational SLAs; drive reduction in operational noise and manual intervention
• Work closely with product owners and stakeholders to identify and mitigate potential data challenges
• Support the adoption of AI and LLM-based tooling to improve engineering efficiency and data quality