Data & Analytics Engineering Specialist

Job Purpose

Sunbet, the online gaming and sports betting division of Sun International, supports the group’s digital gaming strategy by driving product performance, operational efficiency, and revenue growth. The Data & Analytics Engineering Specialist is a senior individual contributor within the Insights team, responsible for transforming raw, ingested data into trusted, analytics-ready datasets that enable efficient reporting, insight generation, and data-driven decision-making. This role operates between the Integrations team and the Insights team. While the Integrations team is responsible for sourcing, ingesting, validating, and ensuring the integrity of raw data within the operational data store and data warehouse, the Data & Analytics Engineering Specialist owns the analytics data layer. This includes building curated tables, aggregated datasets, semantic models, and analytical views that improve performance, reduce cost, and ensure consistent, trusted metrics across the business.

Job Scope

Analytics Data Platform & Modelling

• Design, build, and maintain curated analytics tables and views derived from raw ingested data to support reporting, analysis, and advanced analytics.
• Develop aggregated and pre-calculated datasets across multiple data sources to improve query performance and reduce repeated aggregation on raw data.
• Create reusable analytical datasets that introduce additional layers of insight for reporting and decision-making purposes.
• Translate analytical and business requirements into scalable, well-documented data models that act as a single source of truth.
• Optimise table structures, joins, and query patterns to ensure analytics solutions are performant and scalable

 Semantic Models & BI Enablement

• Work closely with BI developers to design, optimise, and maintain semantic models that support efficient, reliable, and scalable reporting.
• Ensure semantic models are refreshed appropriately and place minimal strain on the underlying data warehouse.
• Support best-practice data modelling and refresh strategies to improve dashboard performance and user experience.
• Partner with BI teams to ensure reporting solutions are built on trusted, analytics-ready data foundations.

Data Quality, Metrics & Single Source of Truth

• Define, implement, and maintain clear and consistent business metrics at the analytics layer.
• Ensure a single source of truth exists for core metrics used across reports, dashboards, and analytical outputs.
• Partner with analysts and stakeholders to align metric definitions and prevent duplication or conflicting logic.
• Maintain clear documentation of data models, transformations, and metric definitions

Performance, Cost & Platform Optimisation

• Design and manage data processing, aggregation, and refresh strategies to minimise unnecessary load on the data warehouse.
• Monitor and optimise data processing, storage, and data movement to manage platform costs effectively.
• Ensure table refresh frequencies are aligned with business needs and system capacity.
• Continuously assess and improve database design, table structures, and data flows to balance performance, cost, and scalability.

 AI & Advanced Analytics Enablement

• Support the design and maintenance of data models and analytical datasets required for AI agents and advanced analytics use cases.
• Ensure data structures are suitable for machine learning, automation, and AI-driven insights.
• Work with analytics and AI teams to enable scalable, high-quality data inputs for intelligent systems.

 Collaboration & Continuous Improvement

• Work closely with the Integrations team to manage schema changes, upstream dependencies, and data availability.
• Partner with analysts, BI developers, and stakeholders to continuously improve analytical capability and insight delivery.
• Identify opportunities to improve analytics tooling, processes, and standards.
• Maintain strict confidentiality and protect business data.

Qualifications

• Bachelor’s degree in computer science, Data Engineering, Information Systems, Engineering, Mathematics, Statistics, Actuarial Science, or a related technical field.

Experience

• Minimum 5+ years of experience in data engineering, analytics engineering, or a closely related role.
• Proven experience building analytics-ready data models and aggregated datasets in a cloud data warehouse environment.
• Strong experience working with raw ingested data and transforming it into performant analytical structures.
• Experience supporting BI and analytics teams in a production environment.
• Experience balancing analytical requirements with performance, scalability, and cost considerations

Behavioural Competencies

• Ownership and accountability
• Analytical thinking
• Technical excellence
• Attention to detail and data integrity
• Collaboration and stakeholder engagement
• Commercial awareness and cost consciousness
• Continuous improvement mindset

Technical Competencies

• Advanced SQL, including PostgreSQL, for data transformation, aggregation, and modelling.
• Strong data modelling skills, including star schemas, semantic layers, and aggregated datasets.
• Experience with cloud data platforms and storage solutions.
• Strong understanding of data processing, refresh strategies, and performance optimization.
• Experience designing data structures to support BI, analytics, and AI use cases.
• Strong documentation and communication skills