Senior Analytics Engineer

Role overview A senior analytics engineering role on an Analytics Platform team that treats data as a strategic asset across product, commercial, and engineering functions. The position focuses on building curated datasets, metric layers, and semantic models that power trusted self-service analytics across the organization. You will work embedded with data engineering squads as part of a community of more than 40 data and analytics engineers.

Responsibilities - Design and build curated analytical datasets, metric implementations, and semantic models that enable consistent self-service analytics. - Apply data warehouse modeling techniques to turn evolving product and business needs into scalable, reusable data structures. - Partner with product, data science, 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 single source of truth. - Contribute to data quality, observability, lineage, and documentation practices that increase trust and reduce firefighting. - Promote reuse over reinvention by refactoring ad-hoc logic in reporting views or custom SQL into curated, high-quality models. - Raise the engineering bar through reviews, testing, automation, and pragmatic architectural improvements. - Treat semantics as code using version control, testing, automation, and CI/CD to manage the lifecycle of business logic. - Drive self-service adoption by creating foundations that allow analysts and business users to explore data safely. - Collaborate daily with data engineers and analysts on shared foundations and end-to-end delivery.

Requirements - Strong SQL skills and proven experience building analytical datasets and metrics in a modern data warehouse such as Snowflake, BigQuery, or Redshift. - Solid understanding of data warehouse modeling, including dimensional modeling, fact and dimension design, grain definition, slowly changing dimensions, and semantic consistency. - Hands-on experience with reporting and BI tools, ideally Tableau, including how data sources, extracts, and dashboard logic affect correctness and performance. - A production mindset with care for reliability, maintainability, documentation, and operational ownership. - Strong ownership and collaboration skills, with the ability to drive clarity in ambiguous problem spaces and partner across 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. - Familiarity with data cataloging, 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 and event-heavy ecosystems such as Kafka.

Benefits and work setup - Medical and health insurance coverage. - Open annual leave policy. - Access to an Employee Assistance Programme. - Training and learning development support. - Additional benefits vary by country and are shared during the hiring process.