Job Description The role of the Lead Data Engineer is to lead the design, development, and maintenance of the organisation's data infrastructure. The role involves overseeing the efficient, reliable, and secure collection, storage, and processing of data, which is critical for enabling data-driven decision-making across FGFS. The Team Lead will help manage and mentor a team of data engineers, working collaboratively with cross-functional teams to implement data solutions, optimise data pipelines, and drive the overall data strategy, ensuring data integrity, security, and accessibility across FGFS. The Team Lead will also play a key role in strategic planning, process improvement, and aligning data initiatives with business goals. Lead the design, development, and maintenance of scalable and efficient data pipelines for extracting, transforming, and loading data from multiple sources. Oversee the team's efforts in building and optimising data pipelines, ensuring alignment with organisational goals and performance standards. Manage and mentor a team of data engineers, providing guidance, support, and professional development opportunities. Oversee the diagnosis and resolution of complex data-related issues within the platform, ensuring prompt and effective support and maintenance of data systems. Implement best practices for monitoring and alerting to proactively address potential issues. Act as a primary liaison between the data engineering team, spoke analysts, and other key stakeholders to align data solutions with business needs. Ensure the team adheres to data governance policies, regulatory requirements, and data security best practices. Oversee the creation and maintenance of comprehensive documentation of data engineering processes. Regularly report on system performance, data quality, and pipeline health to senior management and other stakeholders. Drive the continuous improvement of data engineering practices by staying current with industry trends, emerging technologies, and best practices. Lead initiatives to implement innovative solutions and improvements, ensuring the data engineering team's approach remains efficient, scalable, and effective.