Senior Manager - Data Engineer.Group Enterprise Management

Mtn Group; Gauteng; On-site; ZAR 1 200 000 - 1 800 000; Full time

Job summary

A leading telecommunications firm in South Africa is seeking a Product Owner/Manager to drive the transformation into a data-powered enterprise. This role involves overseeing AI model production, designing enterprise data infrastructure, and collaborating with cross-functional teams to ensure competitive AI solutions. Candidates should have 8-10 years of experience in data engineering, proven capabilities in productionising AI models, and a strong educational background in relevant fields.

Benefits

Dynamic work environment

Opportunities for professional growth

Comprehensive benefits package

Qualifications

• 8–10+ years in large-scale data engineering with senior-level experience required.
• Proven track record in productionising AI models at enterprise scale.
• Experience building AI/ML infrastructure and pipelines in hybrid environments.

Responsibilities

• Oversee industrialisation of AI models and ensure compliance.
• Lead design and governance of a multi-cloud AI and data estate.
• Partner with cross-functional teams to align AI engineering outputs with business needs.

Skills

Data Pipeline Mastery

Big Data Infrastructure Leadership

Privacy & Governance

Cloud & DevOps Integration

System Optimisation

Cross-functional Influence

Education

Bachelor's degree in Computer Science or related field

Master's degree in Data Engineering or related field

Tools

Spark

Kafka

SQL

Python

Docker

Kubernetes

Job description

Global Influences :

The global economy is undergoing rapid digital transformation, with AI and machine learning emerging as core differentiators for competitive advantage.

Enterprises are embedding predictive intelligence into their operations, products, and customer experiences to enhance efficiency, agility, and revenue growth.

Meanwhile, cloud-native analytics platforms, federated learning, and MLOps frameworks are standardising how AI is scaled across complex, multi-market organisations.

Industry Drivers :

In the telecommunications sector, predictive analytics has become central to driving value from large-scale, real-time data generated by mobile networks, customer usage, and behavioural patterns.

Use cases such as churn prediction, next-best-offer recommendations, network anomaly detection, and location-based targeting are no longer aspirational; they are expected.

As telcos evolve into techcos and platform businesses, the ability to productise and monetise AI capabilities across industries such as FMCG, Retail, finance, and public services becomes a critical growth lever.

Additionally, as data monetisation becomes mainstream, clients are not just asking for raw data but for intelligent insights, predictive signals, and decision-ready outputs all of which require robust data science capacity.

Organisational Mandate :

MTN DataCo is positioned at the forefront of MTN Group's transformation into a data-powered, AI-native enterprise.

The organisation is building a multi-OpCo data ecosystem capable of scaling advanced analytics across internal functions (marketing, sales, finance, networks) and external markets.

This includes delivering reusable AI models, creating monetisable analytics solutions, and integrating intelligence into products and consulting services.

Within this context, the Product Owner / Manager plays a critical role in translating MTN's DDO strategy into high-impact data intelligence solutions.

These products must serve diverse users, from OpCo teams to multinational clients, while aligning with MTN's consulting services and monetisation roadmaps.

As the product lead, the incumbent must navigate a federated technology landscape, orchestrate agile delivery, and ensure that MTN's data products are competitive, compliant, and commercially viable.

Organization Values :

At MTN we believe that understanding our people's needs and aspirations is key to creating experiences that delight you at work, every day.

We are committed to fostering an environment where every member of our Y'ello Family is heard, understood, and empowered to live an inspired life.

Our values keep us grounded and moving in the right direction.

Most importantly, they keep us honest.

It is not something we claim to be.

It is in our DNA.

As an organisation, we consider it our mission to create an exciting and rewarding place to work, where our people can be themselves, thrive in positivity and ignite their full potential.

A workplace that boosts creativity and innovation, improves productivity, and ultimately drives meaningful results.

A workplace that is built on relationships and achieving a purpose that is bigger than us.

This is what we want you to experience with us!

Our commitments go beyond an organisational promise.

It is in our leadership and managerial ethos to meaningfully partner with our employees, customers, and stakeholders with a vision to realise our shared goals.

Our values dubbed, LIVE Y'ello, are the cornerstone of our culture.

Lead with Care

Can-do with Integrity

Collaborate with Agility

Serve with Respect

Act with Inclusion

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 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.

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

Key Deliverables – 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