Lead Data Engineer- BI & Reporting

The Lead 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 technical solution design and end-to-end fit within the data architecture.  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. 

In this role you will be responsible for below: 

• Lead technical design, development, and deployment for solutions within a team. Carry out effective technical design reviews to ensure that the right architecture patterns are used by the team. Conduct required source system analysis, data profiling, source-to-target mapping activities, and data modeling (both logical and physical) to ensure delivery of business needs.
• Complete data engineering coding and design tasks on problems of high scope and complexity. Demonstrate good coding principles and exercise good judgment in designing and building solutions. Conduct code review for peers. Ensure solutions adhere to published data privacy and cybersecurity principles.
• Implement and practice fit-for-purpose estimation techniques, to promote iterative delivery. Help the team in refining estimates during sprint planning, with optimal assumptions and inter-dependency understanding.
• Operate with a data-driven mindset.  Help translate data and analytics requirements into data solutions based on the approved technical designs. Assist with data analysis activities such as source system analysis and data modeling, to ensure solutions deliver on business needs.  Work with team members to envision solutions as a set of data products and data pipelines.  
• 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.
• Collaborate with members of the cross-functional team to identify areas of inefficiency and propose solutions.  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.
• Update data inventories and registries as required to keep metadata and data lineage up-to-date, following agreed Data Governance standards, guidelines, and principles.
• Partner with fellow team members, Principal Data Engineers, and Architects for developing and implementing data engineering best practices and modernization techniques, 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.
• Work effectively on incident resolution, conducting root cause analysis, and handling business escalations. Engage other technical experts and vendor partners in the timely resolution of incidents.  Maintain regular and transparent communication during incident management.  Conduct post-mortem and lessons-learned discussions.
• Key contributor and catalyst in attracting and retaining data engineering talent. Handhold new joiners to ensure successful onboarding. Support engineering leadership for venturing into different working models for scaling up the data engineering team in line with delivery commitments.