Data Engineering Manager
Summary
The Data Engineering Manager leads a team of engineers to design and deliver scalable data platforms and pipelines. The role involves technical leadership, stakeholder management, and the implementation of modern data practices using cloud technologies, SQL, and Python.
The Data Engineering Manager will lead the data engineering function, driving the design, development and delivery of scalable, secure and high-quality data platforms and products. The role combines technical leadership, people management, engineering delivery and strategic stakeholder engagement, enabling data-driven decision-making, analytics and AI across the organisation.
Key Responsibilities
- Lead, coach and develop a high-performing team of Data Engineers.
- Define and execute the data engineering and platform roadmap aligned to business and technology strategy.
- Oversee the design and delivery of scalable data pipelines, data platforms and data products.
- Drive modern data engineering practices across cloud, automation, DevOps, CI/CD and data quality.
- Ensure data platforms are reliable, secure, scalable and cost-effective.
- Partner with Data Science, Analytics, Architecture, Technology and business stakeholders to enable analytics, AI and machine learning use cases.
- Promote strong practices around data governance, security, quality, metadata and lineage.
- Provide technical direction on data architecture, engineering standards and technology selection.
- Manage delivery priorities, risks, dependencies and stakeholder expectations.
- Drive continuous improvement, innovation and adoption of emerging data technologies.
- Bachelor's degree in Computer Science, IT, Engineering, Data Science or related field.
- 8+ years' experience in data engineering, software engineering, data platforms or related technology disciplines.
- 3+ years' experience leading or managing data engineering teams.
- Proven experience designing and implementing enterprise-scale data platforms and pipelines.
- Strong experience with cloud data platforms, preferably Azure, AWS or GCP.
- Strong SQL and Python skills.
- Experience with technologies such as Databricks, Snowflake, Microsoft Fabric, Spark, Kafka, Airflow or dbt would be advantageous.
- Experience working in Agile, DevOps and CI/CD environments.
- Strong stakeholder management, communication and problem-solving skills.
Advantageous Experience
- Financial services or insurance experience.
- Data lake/lakehouse environments.
- AI/ML and MLOps.
- Data governance and regulatory environments.
- Cloud migration and modernisation of legacy data platforms.