Engineering Manager: Data

SUMMARY:
Gijima Staffing Solutions is recruiting on behalf of a client for an Engineering Manager: Data to join a leading retail organisation’s Information Technology team based in Cape Town, Western Cape.
This is a strategic leadership opportunity for an experienced data engineering professional to lead innovative, hybrid data teams operating across on-premise and cloud environments. The successful candidate will be responsible for designing, building, and optimising scalable, secure, and high-performing data architectures that integrate Oracle Data Warehouse systems with modern AWS cloud data platforms.
The role requires a strong balance of technical expertise, architectural leadership, and people management capability. The successful incumbent will drive the evolution of enterprise data capabilities, ensuring robust governance, high availability, disaster recovery, and continuous improvement across all data engineering practices.

POSITION INFO:
Key Responsibilities Data Architecture and Engineering Leadership Design and implement scalable, secure, and high-performance data architectures across on-premise Oracle environments and AWS cloud platforms. Oversee and optimise Oracle Data Warehouse systems, including ETL processes, OLAP cubes, and data transformation pipelines. Develop and implement modern AWS-based data pipelines using technologies such as S3, Redshift, Glue, Lambda, EMR, Kinesis, DynamoDB, and Athena. Establish best practices for data ingestion, transformation, storage, and retrieval across hybrid environments. Data Pipeline and Platform Development Drive the adoption of modern data engineering technologies including Spark, Kafka, Airflow, and NoSQL solutions. Oversee CI\/CD processes for data workflows, ensuring efficient, reliable, and automated deployments. Manage version control and DevOps practices using tools such as Git, Jenkins, and Terraform. Optimise data processing and storage for cost efficiency, speed, and scalability. Data Governance and Risk Management Implement and maintain data governance, access control, and compliance frameworks across hybrid environments. Ensure robust disaster recovery and high availability strategies for critical enterprise data systems. Maintain data integrity, security, and compliance across all data engineering solutions. Stakeholder Collaboration Partner with data science, analytics, and data visualisation teams to enable advanced analytics and reporting. Engage with business and IT stakeholders across all levels of the organisation. Ensure alignment between data engineering initiatives and business strategy. Minimum Requirements Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or related field. Minimum 6 years of experience in data engineering. At least 2 years' experience leading hybrid (on-premise and cloud) data engineering teams. Strong proficiency in SQL (Oracle, Redshift) and Python for data processing and automation. Strong experience with Oracle Data Warehouse and OLAP environments. Strong experience in AWS cloud data architecture and services. Deep understanding of ETL pipelines, data lakes, data warehouses, and real-time data processing. Experience with CI\/CD pipelines, Git, Jenkins, and Terraform. Strong knowledge of data governance, compliance, and security frameworks. Excellent communication and stakeholder engagement skills. Additional Criteria Strategic thinking and ability to define long-term data engineering vision. Strong problem-solving and troubleshooting capability in complex data environments. Ability to adapt and thrive in fast-paced, evolving technology environments. Strong collaboration and communication skills across technical and business teams. Commitment to continuous learning and innovation. Strong leadership values including integrity, accountability, and collaboration. If you meet the above requirements and are ready to take on a strategic leadership role in a dynamic data environment, we encourage you to apply. If you have not heard from us by 20 June, please consider your application unsuccessful.