Lead Data Engineer Global Grade 12

The Lead Data Engineer is responsible for the hands-on delivery of complex, production grade data engineering solutions, while providing technical leadership, standards, and quality assurance across the data engineering team. The role ensures that all data pipelines, platforms, and models are scalable, secure, well architected, and aligned to enterprise data strategy and governance, while mentoring engineers and driving engineering excellence.

• Lead the design, development, and optimisation of scalable data pipelines and platforms

• Provide technical leadership and act as the final escalation point for complex data engineering decisions

• Define, document, and enforce data engineering standards, patterns, and best practices

• Ensure high quality, reliable, and cost‑efficient data solutions

• Mentor and coach data engineers through code reviews, design sessions, and technical guidance

• Ensure data solutions comply with security, governance, privacy, and architectural standards

• Collaborate closely with analytics, BI, platform, and business stakeholders

• Translate functional requirements to high level design and build technical specifications documents

• Robust, scalable Azure Synapse and Snowflake‑based data pipelines

• High‑quality, maintainable SQL, Python, and Spark code

• Documented engineering standards and architectural patterns

• Improved platform stability, performance, and cost efficiency

• Uplifted data engineering capability and consistency across the team

• Trusted, production‑ready datasets supporting analytics and decision‑making.

• Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field

(Equivalent practical experience considered)

• Extensive hands‑on experience in production data engineering environments.

• Proven experience designing and supporting data warehouses, lakehouses, or analytics platforms.

• Strong experience with Azure Fabric and/or Snowflake with a preference towards Snowflake.

• Experience with CI/CD, Git, and automated data pipelines.

• Advanced SQL and strong Python skills.

• Strong data modelling and performance optimisation capability.

• Technical leadership and mentoring skills.

• Architectural thinking and problem‑solving ability.

• Strong stakeholder communication and collaboration skills.

• High standards for quality, reliability, and governance.