Senior Data Engineer at MTN, Uganda, Kampala

Mission/ Core purpose of the Job 

• To develop and maintain reusable data pipelines used for insights and advanced data analytics solutions that can serve the business needs of the organization.
• To facilitate getting data from a variety of different sources in the right formats, maintaining data quality standards, and assuring that downstream users can get that data quickly

Main Job Functions:

• Drive business engagement with leaders (include BI teams) to support and implement high-quality, data-driven decisions.
• Building automated reports and advanced analytics solutions based on multiple data sources
• Implement advanced ETL and analytics use-case development/ deployment like end-to-end machine learning models
• Develop, test and maintain data pipelines that are scalable, repeatable, and secure and can serve business needs of the organization
• Create a “Model Factory” for Data Science Models and Algorithms to ensure governance.
• Identify useful data and extend advanced analytics use-case development/ deployment like end-to-end machine learning model, advanced analytics, big data visualizations to entire company through BI teams
• Prepare long-term evolution plans for data analytics projects developed in-house
• Innovate and advance on existing data infrastructure as business use cases continue to emerge
• Design and implementation of modern Data APIs for internal and external data access
• Automated reporting and Dashboards
• Advanced analytics and Machine Learning models
• Developing data-marts used for building Credit Scoring models for Micro-lending internally and third-party consumption
• Divisional people leadership activities: coaching, staff development and motivation through intra staff coaching, employee networks mentorship/support, own division employee engagement projects, faculty roles, inter/intra talent mentorship, etc.
• Generic activities necessary for positive business outcomes such as: supporting recruitment, thought leadership as internal (interview/disciplinary) panel member, influencing next level leaders.

Education:

• First degree in any discipline preferably Software Engineering, Information Systems, Computer Science, Statistics or equivalent
• Master’s Degree in a relevant discipline will be an added advantage.

Experience:

• 3+ years of work experience in Software Development and Data Science related projects in large organization.
• Knowledge in cloud computing technologies
• Worked across diverse cultures and geographies advantageous.
• Detailed working experience of data governance and standards.

Competencies:

Training:

• Data Science, Artificial Intelligence, Data Engineering, Machine Learning and Data Analysis
• Computer Science, Software Engineering and Applied Mathematics
• Systems design and testing
• Big Data technologies
• Data Warehousing
• Analytical Thinking
• Business Process Design and Reengineering
• Decision Making
• Information Management
• Prioritization
• Problem Solving
• Project Planning and Control
• Reporting
• Software Development and management
• Strategic Thinking
• System Analysis
• System Design and Integration
• Telecommunications Technology Fundamentals

Functional Competencies

Behavioral Qualities

• Self-motivated
• Decisive
• Independent but a team player 
• Customer oriented. 
• Alert to environment changes and trends. 
• Good attitude and adaptable. 
• Must have attention to details

Leadership Competencies

• Results orientation
• Committed to making a difference
• Credibility
• Building collaborative relationships
• Engaged management
• Developing & empowering
• Influencing, Leadership, Power
• Sound judgment
• Strategic planning
• Risk-taking, Innovation
• Global awareness 
• Planning and Budgeting

NB: Presentation of false academic documents and certification will lead to criminal prosecution. Any form of canvassing will lead to automatic disqualification. Should you not hear from us within fourteen (14) days from the closing date of this advertisement, you may consider your application to be unsuccessful.