Analytics Engineer Mobile

About the Role

The number 1 Top Employer in South Africa. Certified by the Top Employer Institute 2026.

Responsibilities

• Mobile Data Modelling & Transformation: Design, build and maintain robust transformation pipelines and analytics-ready data models for mobile performance reporting. Structure fact, dimension and event-based models using business-friendly naming conventions and reusable design patterns.
• Mobile KPI & Semantic Layer Enablement: Translate approved business definitions into governed measures, calculations and semantic models. Promote consistent interpretation of mobile KPIs across dashboards, reports and analytical use cases, with clear ownership and documentation.
• Data Integration & Reconciliation: Bring together relevant mobile, customer, sales, product and financial data from approved enterprise sources. Support reconciliation across source systems and reporting outputs, identify data breaks and work with upstream owners to resolve root causes.
• Data Quality, Testing & Observability: Implement automated tests, validation rules, anomaly checks, monitoring and alerting for mobile data products. Track quality issues, document known limitations and ensure corrective actions are visible and controlled.
• Analytics Infrastructure & Performance: Optimise data models, queries and refresh patterns for reliability, usability, performance and cost. Build scalable assets that support recurring executive reporting as well as deeper operational analysis.
• Self-Service Analytics Enablement: Create and maintain curated datasets, reporting views, documentation and reusable analytical assets that enable analysts and authorised business users to answer common mobile performance questions with reduced dependency on ad-hoc extracts.
• Reporting Automation Support: Partner with reporting and digitisation specialists to automate mobile reporting workflows and eliminate repetitive manual preparation, manipulation and reconciliation activities.
• Documentation, Metadata & Lineage: Maintain data dictionaries, metric definitions, model documentation, lineage and change records so that data assets are transparent, auditable and easier to support.
• Governance, Security & Compliance: Apply approved data governance, access control, privacy, retention and security requirements to mobile data products. Ensure solutions are developed and operated in line with organisational standards.
• Continuous Improvement: Identify opportunities to improve the mobile analytics stack, engineering practices and delivery processes. Introduce fit-for-purpose automation, reusable components and engineering standards that improve speed and quality.
• Mobile sales and activations performance, including achievement against approved targets.
• Revenue, usage and customer value views based on governed business definitions.
• Customer base movements, retention and churn-related analysis where approved data is available.
• Product, channel, segment, account and regional performance views required by Business Performance.
• Pipeline, forecast and conversion analysis for mobile opportunities where relevant source data is available.
• Data-quality and reconciliation views that make breaks, exceptions and unresolved variances visible.
• Advanced SQL & Data Transformation: Strong capability in SQL for complex transformations, data validation, performance optimisation and the development of production-grade analytics models.
• Analytics Engineering: Practical experience with dbt or an equivalent transformation framework, including modular model design, testing, documentation, dependency management and deployment practices.
• Data Modelling: Experience designing dimensional, star, snowflake and event-based models, with a strong understanding of facts, dimensions, grain, keys, slowly changing dimensions and reusable semantic structures.
• Data Warehousing / Lakehouse: Hands-on experience with modern enterprise data warehouse or lakehouse platforms and the ability to work effectively with large, multi-source datasets.
• Python & Automation: Working proficiency in Python for data manipulation, validation, automation and analytics tooling, using fit-for-purpose libraries and controlled development practices.
• Business Intelligence: Experience enabling self-service analytics and building curated datasets or semantic models for platforms such as Power BI, Qlik or comparable BI tools.
• Version Control & CI/CD: Experience using Git and collaborative development workflows, with exposure to automated testing, code review, release control and CI/CD practices for data solutions.
• Data Quality & Observability: Knowledge of testing frameworks, monitoring, anomaly detection, logging and incident-management practices for analytics pipelines and models.
• Cloud Data Services: Working knowledge of at least one major cloud platform in the context of data storage, transformation, orchestration and analytics services.
• Data Governance: Understanding of data cataloguing, lineage, metadata, access control, retention, privacy and the practical application of governance standards
• Work closely with the Reporting and Digitisation Manager to translate the mobile reporting roadmap into prioritised technical deliverables.
• Partner with reporting specialists, analysts and business stakeholders to clarify requirements, define model grain and agree consistent KPI logic.
• Collaborate with data engineering, IT and Data & Analytics teams on upstream data pipelines, platform standards, access, deployment and production support.
• Engage Sales, Commercial, Finance, Product and operational stakeholders to understand business rules, resolve data-definition gaps and improve trust in reporting outputs.
• Communicate technical concepts clearly to non-technical audiences and explain the impact of data limitations, quality issues and model changes.
• Contribute to peer reviews, technical standards, documentation and knowledge sharing across the reporting and analytics team.
• Provide technical guidance and support to developing team members where required, without displacing the people-management accountability of the Reporting and Digitisation Manager.
• Bachelor's degree (NQF Level 7) in Computer Science, Data Science, Information Systems, Statistics, Engineering or a related field, or equivalent relevant practical experience.
• Approximately 3-7 years of relevant experience in analytics engineering, data engineering, business intelligence engineering or a closely related data role, with demonstrated responsibility for production-grade data models and analytics infrastructure.
• Proven experience building, testing, documenting and maintaining analytics-ready data models used by business stakeholders.
• Strong SQL capability and practical experience with data transformation, dimensional modelling and warehouse or lakehouse environments.
• Experience integrating data from multiple enterprise systems and resolving data consistency or reconciliation challenges.
• Experience working cross-functionally with technical and non-technical stakeholders in a large or complex organisation.
• Ability to explain complex data concepts clearly and drive alignment on metric definitions, data standards and solution design.
• Telecommunications or enterprise B2B experience, with exposure to mobile products, customer, sales, usage or revenue data.
• Experience with orchestration tools and scheduled data workflows.
• Exposure to streaming or near-real-time analytics use cases.
• Knowledge of data cataloguing, lineage and observability tools.
• Experience with infrastructure-as-code or cloud deployment practices.
• Professional certification in cloud data engineering, analytics, BI, data management or a related discipline.

Requirements

• Ownership & Accountability: Takes end-to-end responsibility for the quality, reliability and usability of assigned data products.
• Analytical Rigour: Applies structured problem-solving, validates assumptions and pays close attention to data grain, logic and exceptions.
• Business Curiosity: Seeks to understand the business decision behind a request and translates it into a sustainable data product rather than a once-off extract.
• Collaboration & Influence: Builds constructive relationships across technical and business teams and can influence alignment on standards without relying on hierarchy.
• Pragmatic Delivery: Balances technical excellence with business urgency, choosing scalable solutions while delivering value iteratively.
• Communication: Explains data models, limitations, risks and recommendations clearly to different stakeholder groups.
• Continuous Learning: Keeps current with analytics engineering methods, modern data platforms and automation opportunities.
• Integrity & Confidentiality: Handles commercially sensitive and customer-related data responsibly and in accordance with approved controls.
• Enticing incentive programs and competitive benefit packages
• Retirement funds, risk benefits, and medical aid benefits
• Cell phone and data benefits, advantages fibre connection discounts, and exclusive staff discounts offered in collaboration with partner companies