Senior Analytics Engineer

Role overview Design and build curated analytical datasets, metric implementations, and semantic models that enable consistent self-service analytics across a sports, gaming, and fan-experiences technology platform. The role sits within a mature analytics community and partners closely with product, data science, and commercial stakeholders to turn evolving business needs into reliable, reusable data structures.

Responsibilities - Design and build curated analytical datasets, metric implementations, and semantic models that support consistent self-service analytics across the business. - Turn evolving product and commercial needs into scalable, reusable data warehouse models through hands-on dimensional modelling. - Partner with product, data scientists, and business stakeholders to clarify requirements and translate them into reliable data models and reporting-ready outputs. - Build and own production Tableau dashboards on top of curated datasets and metric definitions, ensuring correctness, performance, and a consistent single source of truth. - Strengthen data quality, observability, lineage, and documentation practices to increase trust and reduce firefighting. - Identify ad-hoc logic in reporting views or custom SQL and refactor it into curated, high-quality models that multiple teams can rely on. - Treat semantics as code using version control, testing, automation, and CI/CD to manage the lifecycle of business logic.

Requirements - Strong SQL skills with proven experience building analytical datasets and metrics in a modern data warehouse such as Snowflake, BigQuery, or Redshift. - Solid understanding of data warehouse modelling best practices, including dimensional modelling, fact/dimension design, grains, slowly changing dimensions, and semantic consistency. - Experience with reporting and BI tools such as Tableau, including how data sources, extracts, and dashboard logic impact correctness, performance, and trust. - A production mindset with care for reliability, maintainability, documentation, and operational ownership beyond one-off dataset creation. - Strong ownership and collaboration skills with the ability to drive clarity in ambiguous problem spaces and partner with both technical and non-technical teams. - Excellent communication skills and a pragmatic, problem-solving approach.

Nice to have - Experience with orchestration and workflow tooling such as Airflow or similar. - Experience with data cataloguing, lineage, and governance tooling such as DataHub. - Background in product-led, experimentation-driven, or high-growth environments where definitions evolve quickly. - Experience supporting downstream operational consumers such as CRM or audience platforms, or ML and feature engineering use cases. - Familiarity with streaming or event-heavy ecosystems such as Kafka.

Benefits and work setup - Medical or health insurance, open annual leave, employee assistance programme, and training and learning development support, with additional benefits varying by country and shared during the hiring process.