Job Description

The Engineering Manager: Data leads two dynamic teams - one focused on on-premise data solutions and the other specialising in AWS-based cloud data engineering. This role is about more than just managing data systems, it is about shaping the future of how the organisation leverages data to drive insights, innovation, and efficiency. By ensuring seamless integration between on-premise and cloud environments, the Engineering Manager: Data enables the business to make informed, data-driven decisions while optimising performance and scalability.
Job description

• Design and implement scalable, secure, and high-performing data architectures across both on-premise and AWS-based cloud platforms.
• Establish best practices for data ingestion, transformation, storage, and retrieval, ensuring efficient and reliable data processing.
• Oversee and optimise ETL processes for the Oracle Data Warehouse, while leading the adoption of modern data pipeline architectures in AWS.
• Ensure strong data governance, security, and compliance frameworks to maintain data integrity and accessibility across both environments.
• Optimise data processing and storage for cost efficiency, speed, and scalability.
• Partner with data science, and data visualisation engineering teams to support advanced analytics and reporting needs.
• Drive the adoption of modern data technologies such as Spark, Kafka, Airflow, and NoSQL solutions, balancing innovation with stability.
• Oversee CI/CD processes for data pipelines, ensuring agile, efficient deployments across both teams.
• Develop and mentor a team of high-performing data engineers, fostering a culture of collaboration, innovation, and growth.
• Implement disaster recovery and high availability strategies, ensuring business continuity for critical data systems across Oracle and AWS.

Minimum requirements

• Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field.
• Minimum 6 years of experience in data engineering, with at least 2 years leading hybrid (on-premise & cloud) data teams.
• Proficiency in SQL (Oracle, Redshift), and Python for data processing and automation.
• Strong experience with on-premise Oracle Data Warehouse and OLAP cubes.
• Expertise in AWS cloud data architectures (S3, Redshift, Glue, Lambda, EMR, Kinesis, DynamoDB, Athena).
• Deep understanding of ETL pipelines, data lakes, data warehouses, and real-time stream processing.
• Experience with CI/CD for data workflows and version control (Git, Jenkins, Terraform).
• Knowledge of data governance, access control, and compliance frameworks across hybrid environments.
• Ability to lead and mentor two teams of data engineers, fostering collaboration and professional growth.
• Strong capability to translate technical solutions into strategic business value.
• Excellent communication skills, with the ability to engage stakeholders across various levels of the organisation.

ADDITIONAL CRITERIA

• Strategic Thinking: Ability to define and execute a long-term vision for data engineering in both on-premise and cloud environments.
• Collaboration & Communication: Works effectively with business and IT teams to ensure seamless integration of data solutions.
• Problem-Solving: Proactively identifies and resolves challenges in data processing and infrastructure.
• Continuous Learning: Keeps up with the latest developments in data engineering, cloud technologies, and industry best practices.
• Adaptability: Thrives in a fast-moving, evolving technology landscape, adjusting strategies as needed.
• Cultural Fit: Demonstrates integrity, accountability, and a commitment to fostering a collaborative and data-driven organisation.

Skills Required