Data Engineering Team Leader

Position Information

Department/Section: Big Data and Analytics

Job Title: Data Engineering Team Leader

Reports to: Big Data Manager

Job Purpose

Data Engineering Team Leader is responsible and accountable for Big Data Manager to the technology selection, Proof of Concept (POC) execution, design, development, release, and operations of the overall data platform and data warehousing solutions that support the company's business intelligence and decision-making requirements.

Key Tasks/Accountabilities

• Translate Business Needs: Convert stakeholder needs into data system requirements and designs, identifying opportunities for platform use and new technology.

• Develop & Evaluate Systems: Build real-time, batch data processing systems, and actively evaluate/propose new ETL/ELT technologies.

• Provide Technical Vision: Set the corporate leadership, vision, and strategic direction for the data engineering practice and data warehouse.

• Manage POCs & Knowledge: Conduct technology Proofs of Concept (POCs) for new tools and build the operational knowledge base.

• Enable Analytics: Collaborate with BI/Analytics teams to enhance platform features and design/build data marts/models.

• Lead & Develop Team: Effectively manage, motivate, and develop the Data Engineering team resources, ensuring issue resolution.

• Ensure Audit Compliance: Coordinate with internal/external audit functions and follow up on recommendation implementation.

• Maintain Documentation: Record and maintain system data models, processes, workflows, and configuration changes.

• Manage Platform Upgrades: Lead the upgrade of data platform components and service releases to maintain stability and currency.

• Manage Vendor Relations: Manage relationships and agreements with external contractors, suppliers, and partners.

• Define Data Architecture: Focus on physical data modelling, metadata, data pipeline architecture, optimization, and recovery strategy design.

• Ensure Compliance: Adhere to processes, procedures, and regulatory requirements, including Information Security policies.

• Conduct performance reviews, provide ongoing feedback, and support employee development and capability-building initiatives.

• Prepare operational reports, analyse big data trend and performance s, provide recommendations for improvement.

• Contribute to the effective management, motivation, development of the team resources by liaising closely with the key line management, personnel and other relevant parties within Data and AI/Big Data to ensure problems/ issues are identified and resolve or apparently escalated at an earlier stage.

• Lead, coach, develop, mentor the team, ensuring high performance, technical competencies, and adherence to operational standards.

• Ensure compliance with all applicable policies & regulatory requirements in information security.

Qualifications/Experience/Skills

Qualifications/Experience: -

• B.Sc. in Computer/Telecommunication Engineering/IT or Computer Science or any relevant discipline.

• Previous experience in management or leadership roles is preferred.

• Mobile Telecommunications background is desirable.

• Minimum 3 years of work experience in telecom enterprise data engineering and platforms is required.

• In-depth knowledge and experience in Big Data systems is required.

Skills: -

• Ability to interpret and apply best practice methods/processes.

• Excellent numeracy and analytical skills

• Management of external suppliers and software/hardware vendors

• Conflict handling and resolution skills

• Strong follow up and decision-making skills.

• Change management.

• Highly developed communication and reporting skills (verbal and written) in both Arabic and English

• Project management

• Highly developed communication and reporting skills (verbal and written) in both Arabic and English.

• Highly developed skills in use of relevant IT packages including Excel. Word, PowerPoint etc.

• Ability to impart knowledge to others in a 1-2-1 and 1-2-many environments.

Typical Performance Measures

• Quality of Team Contribution.

• Effective Team Lead.

• Highly available platforms and data pipeline latency (as per SLA).

• Successful execution and documentation of Technology POCs.

• Quality of relationships with internal customer’s/peers/vendor support.

• Effectiveness of issue resolution/decision making/change control, and risk management.

• Delivery of outcomes to agreed quality standards and deadlines.

• Adaptability of work plans/resources in response to change.

• Consistency and Quality of running services and solutions.