Senior Data Engineer – Build Scalable Data Platforms

About the role

The Senior Data Engineer will design, build, and operate scalable and reliable data platforms, pipelines, and integration services for Africell Group. This role implements the data architecture and standards defined by the Data Manager and delivers trusted data for reporting, analytics, AI, and business applications.

Key responsibilities

• Design and implement batch, near‑real‑time, and real‑time ETL/ELT pipelines across Telecom, Mobile Money, CRM, Digital, Network, Finance and external systems.
• Build data lakes, lakehouses, data warehouses, data marts and reusable data services.
• Develop Customer 360 datasets and trusted data products for analytics, AI and digital applications.
• Maintain data models, source mappings, metadata, lineage and technical documentation.
• Implement data governance, quality, security, privacy, access‑control and retention standards.
• Monitor data‑quality controls, pipeline health, accuracy, freshness and reconciliation exceptions.
• Build reusable connectors, APIs and governed data services for internal and external consumption.
• Collaborate with analysts, AI engineers and data scientists to create curated datasets and feature pipelines for model training and inference.
• Support AI/ML and Generative AI pipelines, including RAG document ingestion, embeddings, vector indexing and governed access.
• Mentor junior engineers, review designs and promote engineering standards across the group.

Required profile

• Bachelor’s degree in Computer Science, Data Engineering, Information Systems or related field.
• 6+ years of experience in data engineering, data integration or data platform delivery.
• Proven hands‑on delivery of production data pipelines and platforms.
• Experience in high‑volume transactional environments such as Telecom, Mobile Money or FinTech is a plus.

Required skills

• SQL and Python for data integration, transformation and automation.
• Design and operation of data warehouses, data lakes or lakehouses, including dimensional modeling and modern storage formats.
• Workflow orchestration with Apache Airflow.
• Streaming/event processing with Apache Kafka or equivalent.
• Change Data Capture, incremental processing, schema evolution and recovery mechanisms.
• Data quality, metadata, lineage, governance, security and access‑control practices.
• Version control (Git), CI/CD, automated testing, monitoring and production support.
• Exposure to AI/ML pipelines, feature engineering, MLOps, RAG, vector databases and governed data access for AI applications.