Data Engineering Manager

Old Mutual Insure; Johannesburg; On-site; ZAR 1,000,000 - 1,800,000; Full time

Job summary

Old Mutual Insure is seeking a Data Engineering Manager to lead the data engineering function and deliver scalable data platforms that power analytics and AI. You will guide technical direction, manage a high-performing team, and partner with data science, architecture and business to enable data-driven decisions.

The role focuses on cloud data platforms, data governance and modern engineering practices across DevOps, CI/CD and security. Strong leadership and stakeholder management are essential.

Qualifications

• 8+ years in data engineering or related tech.
• 3+ years leading data engineering teams.
• Proven experience delivering enterprise-scale data platforms and pipelines.
• Strong cloud platform experience (Azure/AWS/GCP).
• Strong SQL and Python skills.

Responsibilities

• Lead, coach and develop a high-performing data engineering team.
• Define and execute the data engineering roadmap aligned to strategy.
• Oversee design and delivery of scalable data pipelines and platforms.
• Drive modern data engineering practices across cloud, CI/CD and data quality.
• Ensure platforms are secure, reliable and cost-effective.
• Partner with data science, analytics, architecture and business to enable AI use cases.
• Promote data governance, security, quality, metadata and lineage.
• Provide technical direction on architecture and technology selection.
• Manage delivery priorities, risks and stakeholder expectations.
• Drive continuous improvement and adoption of emerging data technologies.

Skills

Team leadership

Stakeholder management

SQL

Python

Cloud architecture

DevOps / CI/CD

Data governance

Problem solving

Education

Bachelor's degree in Computer Science / IT / Engineering / Data Science or related field

Tools

Databricks

Snowflake

Microsoft Fabric

Spark

Kafka

Airflow

dbt

Job description

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.