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Role Purpose/Business Unit:

The Analytics Engineer - Mobile is responsible for designing, building and maintaining the analytics-ready data products that enable accurate, timely and trusted mobile performance reporting across Business Performance. The role translates raw and lightly processed mobile data into governed data models, reusable metrics, semantic layers and self-service analytics assets that support executive reporting and operational decision-making.

Reporting to the Reporting and Digitisation Manager: Business Performance, the role provides the technical analytics engineering capability required to strengthen the single source of truth for mobile performance. It works at the intersection of data engineering, business intelligence and performance analytics, ensuring that mobile data is reliable, well documented, scalable and fit for use across dashboards, recurring reporting and business analysis.

The role will contribute to faster time-to-insight, reduced manual reconciliation, consistent KPI definitions, stronger data governance and improved stakeholder confidence in mobile reporting. It also supports the broader reporting automation and digitisation agenda by creating reusable data assets that can be consumed efficiently by analysts, reporting specialists and business stakeholders.

Role Boundaries & Accountability

The Analytics Engineer - Mobile is accountable for the technical design and ongoing reliability of assigned analytics models and data products. The role does not independently approve business KPI definitions, source-system ownership or enterprise governance policy. These are agreed with the Reporting and Digitisation Manager and the relevant business, data and governance owners. The role is expected to identify risks, recommend solutions and provide clear evidence to support decisions.

Your responsibilities will include:

• 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 Analytics Scope

The role will support the development of trusted analytics products for mobile performance. The exact KPI catalogue and source-system scope will be governed by approved Business Performance definitions and priorities. Typical analytical domains may include:

• 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.

Technical & Analytical Capabilities

• 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

Stakeholder Collaboration & Ways of Working

• 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 s