MTN Nigeria is part of the MTN Group, Africa\'s leading cellular telecommunications company. On May 16, 2001, MTN became the first GSM network to make a call following the globally lauded Nigerian GSM auction conducted by the Nigerian Communications Commission earlier in the year. Thereafter the company launched full commercial operations beginning with Lagos, Abuja and Port Harcourt. MTN paid $285m for one of four GSM licenses in Nigeria in January 2001. To date, in excess of US$1.8 billion has been invested building mobile telecommunications infrastructure in Nigeria. Since launch in August 2001, MTN has steadily deployed its services across Nigeria. It now provides services in 223 cities and towns, more than 10,000 villages and communities and a growing number of highways across the country, spanning the 36 states of the Nigeria and the Federal Capital Territory, Abuja. Many of these villages and communities are being connected to the world of telecommunications for the first time ever. The company\'s digital microwave transmission backbone, the 3,400 Kilometre Y\'elloBahn was commissioned by President Olusegun Obasanjo in January 2003 and is reputed to be the most extensive digital microwave transmission infrastructure in all of Africa. The Y\'elloBahn has significantly helped to enhance call quality on MTN network.
Mission:
- Build high-quality data pipelines that drive analytic solutions.
- Connect and model complex distributed data sets to build repositories, such as data warehouses and data lakes, using appropriate technologies.
- Manage data-related contexts ranging across addressing small to large data sets, structured, unstructured, or streaming data, extraction, transformation, curation, modeling, building data pipelines, identifying the right tools, writing SQL, Java, or Scala code, etc.
Description:
- Design, develop, optimize, and maintain data architecture and pipelines that adhere to ETL principles and business goals.
- Assemble large, complex data sets that meet functional / non-functional business requirements.
- Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc.
- Build the infrastructure required for optimal extraction transformation and loading of data from a wide variety of data sources using SQL and AWS ‘big data’ technologies.
- Build analytics tools that utilize the data pipeline to provide actionable insights into customer acquisition, operational efficiency and other key business performance metrics.
- Keep data secure.
- Lead the evaluation, implementation and deployment of emerging tools and process for analytic data engineering in order to improve our productivity as a team.
- Work with data and analytics experts to strive for greater functionality data systems.
- Develop and deliver communication and education plans on analytic data engineering capabilities, standards, and processes.
- Partner with business analysts and solutions architects to develop technical architectures for strategic enterprise projects and initiatives.
Education:
- First degree in Mathematics, Statistics, MIS, Computer Science, Engineering, or other related disciplines.
- Fluent in English
Experience:
3-7 years' experience working in data engineering or architecture role.
- Deep knowledge in data architecture, defining data retention policies, monitoring performance and advising any necessary infrastructure changes.
- Expertise in SQL and data analysis experience with at least one programming language.
- Experience developing and maintaining data warehouses in big data solutions.
- Experience with developing solutions using cloud computing services and infrastructure in the data and analytics space (preferred)
- Database development experience using Hadoop or Big Query and experience with a variety of relational, NoSQL, and cloud database technologies.
- Worked with BI tools such as Tableau, Power BI, Looker, Shiny
- Conceptual knowledge of data and analytics, such as dimensional modeling, ETL, reporting tools, data governance, data warehousing, structured and unstructured data.
- Comfortable in dashboard development (Tableau, PowerBI, Qlik, etc.) and in developing data analytics models (R, Python, Spark)
- Big Data Development experience using Hive, Impala, Spark, and familiarity with Kafka (Preferred)
- Exposure to machine learning, data science, computer vision, artificial intelligence, statistics, and/or applied mathematics.
Method of Application
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