Data Engineering Manager

Leadership People Management

• Lead, coach, and mentor a team of data engineers, focusing on career growth, learning, and engagement.
• Take ownership of people management, including development plans, performance reviews, and progression pathways.
• Partner with the Principal Data Engineer to balance technical depth with delivery accountability, ensuring the team excels in both.
• Foster a culture of collaboration, inclusion, and innovation within the data engineering function.

• Oversee the design and implementation of scalable, high-quality data pipelines on AWS and Snowflake.
• Collaborate with the Principal Data Engineer on technical architecture and standards, ensuring solutions are future-proof and cost-efficient.
• Drive adoption of best practices in data engineering, CI/CD, and agile delivery.
• Ensure compliance with governance, security, and data quality requirements.
• Continuously optimize data flows to support business analytics, personalization, and advanced data products.

• Work with Product Owners, Data Governance, and Analytics teams to align engineering work to strategic data goals.
• Communicate progress and solutions effectively to technical and non-technical stakeholders.
• Represent data engineering in cross-functional forums, advocating for modern, reliable, and business-enabling data solutions.
• Contribute to shaping the Data Analytics roadmap, ensuring scalability for future growth.

• Proven experience leading and developing high-performing data engineering teams, ideally across hybrid or distributed environments.
• Strong focus on people management setting clear expectations, supporting individual growth, and ensuring structured career progression.
• Ability to foster a culture of learning, inclusion, and psychological safety, empowering engineers to innovate and take ownership.
• Skilled in coaching engineers of varying experience levels, providing both technical mentorship and constructive feedback.
• Experience partnering with a Principal Data Engineer or equivalent to maintain balance between technical excellence and delivery execution.

• Deep understanding of modern data architecture patterns - Data Lake, Data Warehouse, Lakehouse, and Data Mesh.
• Proven experience designing and delivering cloud data platforms (preferably AWS and Snowflake).
• Expertise in ETL/ELT development, orchestration, and data automation using tools such as AWS Glue, Step Functions, Airflow, or dbt.
• Strong programming proficiency in Python and SQL, with experience in testing, optimisation, and CI/CD integration.
• Experience with streaming and event-driven architectures (e.g., Kafka, Kinesis).
• Knowledge of infrastructure-as-code (Terraform, CloudFormation) and data observability best practices.
• Strong background in data modelling (dimensional, canonical, and semantic models) to support analytical and operational use cases.
• Understanding of data governance, security, and privacy principles, including GDPR compliance, data lineage, and access controls.