Principal Data Engineer

The Principal Data Engineer is a fully-participating member of a cross-functional team working autonomously on technology development and problem resolution in the Enterprise Data & Analytics space. The role involves the design, development, testing, implementation, support and maintenance of technical data and analytic solutions and products that support Emirates Airlines and the Emirates Group businesses - with a focus on complex technical solution design and efficiency in delivering data products and analytical solutions. The position demands in-depth expertise in the respective technology profiles, and involves collaboration with business stakeholders, product owners, delivery leads, architects and site reliability engineers to deliver high-quality product features successfully, and in a timely fashion. The Principal Data Engineers leverages his technology/leadership skills for talent development, and to drive continuous improvement in the form of new data platforms and automation frameworks. Job Outline: - Lead the discovery phases and technical designs for medium to large data and analytical solutions across multiple teams. Carry out effective technical design reviews to ensure that the right architecture patterns are used by the team. Drive proof of concepts and prototypes to validate ideas. - Complete data engineering coding and design tasks on problems of high scope and complexity, in particular for building frameworks that promote automation and reusability. Set up and configure new technology platforms, tools and libraries. Demonstrate good coding principles and exercise good judgement in designing and building solutions. Conduct solution design reviews for peers. Ensure solutions adhere to published data privacy and cybersecurity principles. - Implement and practice fit-for-purpose estimation techniques, to promote iterative delivery, both at the sprint/story level and the initiative/epic level. Help the program team in refining estimates, with optimal assumptions and inter-dependency understanding. Mentor and coach engineers on effective estimation techniques. - Operate with a data-driven mindset. Work with team members to envision solutions as a set of data products and data pipelines. Ensure teams are set up to continuously enhance analytical solutions based on new business requirements, without the need for re-training and re-work. - Ensure solutions being built are stable, scalable and maintainable. Enable test automation and ensure CI/CD pipelines are in good health. Implement monitoring of data applications and track product quality, performance and stability. Drive corrective, adaptive, preventative and perfective actions to maintain solution reliability and quality. - Provide technical leadership to members of the cross-functional team address areas of inefficiency and resolve technical issues. Identify activities resulting in optimal resource utilization, cost reduction, technical debt remediation, service improvement and reuse value. Partner with architects and product owners to prioritize and implement such activities. - Ensure teams keep data inventories and registries updated, following agreed Data Governance standards, guidelines and principles. Help shape Data Governance guidelines, engineering standards, data analysis templates and knowledge base. - Champion development of best engineering practices and modernization techniques. Partner with Enterprise Data & Analytics leadership team and Enterprise Architects for developing and implementing said practices, as well as promoting solution reusability, process automation, built-in-quality, test-driven development, agile delivery, timely root cause analysis and blameless incident post-mortems. Key contributor in building and adopting data engineering playbooks for the relevant technologies, and ensuring adherence to said playbooks, as well as other published coding standards data technology blueprints. - Mentor and coach data engineers on writing efficient code, and automating test suites and other development frameworks. Manage some less senior data engineers guide them through a career development framework. Build fit-for-purpose on-boarding guides and robust feedback mechanisms to ensure optimal experience for data engineering talent and continuous improvement. Help identify any skills gaps and support staff movements and rotations.