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.

• 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.

• Strong SQL skills with proven experience building analytical datasets and metrics in a modern data warehouse such as Snowflake, Big Query, 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.

• Experience with orchestration and workflow tooling such as Airflow or similar.
• Experience with data cataloguing, lineage, and governance tooling such as Data Hub.
• 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.

• 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.