Senior Manager - Data Engineer.Group Enterprise Management at MTN

Responsibilities

AI Model Productionisation & Deployment

• Oversee the industrialisation of AI models, ensuring enterprise readiness, performance optimisation, and regulatory compliance.
• Establish robust MLOps frameworks that support versioning, monitoring, drift detection, and automated retraining across diverse market contexts.
• Translate raw network and customer data into ADR structures optimised for analytics APIs, visualisation layers, and external data services.
• Optimise storage tiering (hot vs cold vs archival), processing costs, and compute scheduling for high-volume workloads.

Enterprise Data & AI Infrastructure

• Lead the design and governance of a multi-OpCo, multi-cloud AI and data estate that supports real-time, high-volume telco, geospatial, and behavioural data streams.
• Ensure infrastructure is optimised for scalability, cross-market interoperability, and commercialisation.
• Develop production-grade, reusable pipelines (batch + streaming) for CDRs, DPI, location, financial, and digital channels.
• Architect and operationalise cloud-native (GCP / Azure / on-prem hybrid) data platforms supporting ingestion, transformation, and ADR creation.
• Drive automation, metadata cataloguing, and data quality frameworks leveraging tools like Databricks, Airflow, BigQuery, and Terraform.

Scalability & Reliability Leadership

• Anticipate and solve complex performance bottlenecks in high-throughput AI workloads.
• Champion cost optimisation, resilience engineering, and observability practices to guarantee uptime and trusted AI delivery.
• Lead a team of data engineers, guiding design reviews, CI/CD standards, and alignment with Data Science, Privacy, and Legal teams.
• Cross-Functional Orchestration
• Partner with Group CIOs, Data Science, Product, Networks, and Commercial leadership to align AI engineering outputs with business-critical use cases and monetisation pathways.
• Act as a trusted advisor to OpCos, ensuring rapid adoption of production-ready AI capabilities.
• Integrate IAM, KMS, VPC Service Controls, and data lineage logging to meet POPIA / GDPR / ISO 27001 standards.
• Innovation & Thought Leadership
• Drive continuous innovation in geospatial AI, telco intelligence, and federated learning frameworks to maintain MTN’s competitive edge.
• Position MTN DataCo as a leading AI engineering hub, setting benchmarks for responsible, explainable, and ethical AI adoption.

Key Deliverables

• Production-grade, monitored, and retrainable AI models serving both internal (OpCos, Group functions) and external (enterprise clients, consulting engagements) markets.
• Standardised MLOps toolkits, reusability frameworks, and onboarding assets to accelerate AI deployment.
• Scalable AI-enabled platforms that support predictive and prescriptive use cases across telco and adjacent industries.
• Performance scorecards measuring AI reliability, deployment speed, cost efficiency, and market impact.

Qualifications

Education:

• Bachelor’s degree in Computer Science, Software Engineering, Information Systems, or a related technical discipline (required)
• Master’s degree in Data Engineering, Cloud Infrastructure, or Big Data Architecture (preferred)
• Industry certifications in cloud platforms (Azure, GCP, AWS), big data frameworks (Spark, Hadoop), or DevOps/DataOps tools are strongly advantageous

Experience:

• 8–10+ years in large-scale data engineering, with at least 5 years in a senior or lead capacity.
• Proven track record in productionising AI models at enterprise scale, ideally in telco, geospatial, or similarly high-volume domains.
• Demonstrated leadership in building AI/ML infrastructure and CI/CD pipelines in complex, hybrid environments.
• Experience influencing C-level stakeholders and translating data/AI strategy into operational and commercial outcomes.

Competencies:

• Data Pipeline Mastery: Expert in building and scaling real-time and batch data pipelines using Spark, Kafka, SQL, and Python
• Big Data Infrastructure Leadership: Deep knowledge of distributed systems (Hadoop, Databricks, Hive) and hybrid cloud environments
• Privacy & Governance: Data Anonymisation & Privacy Techniques (k-Anonymity, Differential Privacy)
• Cloud & DevOps Integration: Skilled in CI/CD, containerisation (Docker, Kubernetes), IaC, and observability tools for data systems
• Governance & Compliance Alignment: Designs pipelines that embed data lineage, security tagging, access control, and policy enforcement
• System Optimisation: Drives performance tuning, cost efficiency, fault tolerance, and workload automation at scale
• Team Enablement: Mentors data engineers, drives capability uplift across OpCos, and standardises reusable engineering components
• Cross-functional Influence: Proactively engages with IT, security, architecture, and analytics functions to accelerate delivery and integration

Key Deliverables:

Internal:

• Production-grade, scalable data pipelines for telco, geospatial, and behavioural datasets
• Observability dashboards, automated recovery scripts, and runbooks for performance and system health
• Internal artefacts for reusability: transformation scripts, standard schema libraries, onboarding guides
• Metrics reports on pipeline uptime, latency, cost performance, and consumption across OpCos

External:

• Integration artefacts and onboarding toolkits for third-party and client data sources
• AI/analytics-ready feature sets delivered to downstream product, consulting, and data science teams
• Technical documentation and data pipeline references embedded into data monetisation offers
• Collaborative PoV datasets and artefacts aligned with consulting engagements or client delivery tracks

Apply Before 12/31/2025