Data & Analytics Engineering Specialist at Sun International

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.

Job Requirements

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