Analytics Engineer

Who you are

• You have 2+ years of experience in analytics engineering, data engineering, or a related field
• You have strong SQL skills and experience with data modelling
• You are experienced with dbt (or similar SQL-based transformation frameworks) and a cloud data warehouse such as BigQuery, Snowflake, Redshift, or Databricks SQL
• You are familiar with workflow orchestration tools such as Airflow, Dagster, Prefect, or Flyte
• You care about data quality, reliability, and testability
• You are comfortable working with BI/visualisation tools such as Looker or Tableau
• You communicate clearly with both technical and non-technical partners
• You are able to prioritize and deliver in a fast-moving environment
• You have experience with platform or developer productivity data, experimentation, or ML/AI metrics

What the job involves

• The Platform team creates the technology that enables Spotify to learn quickly and scale easily, enabling rapid growth in our users and our business around the globe. Spanning many disciplines, we work to make the business work; creating the infrastructure, tooling, frameworks, and capabilities needed to welcome a billion customers
• We’re looking for an Analytics Engineer II to join Spotify's Platform Central Data (PCD) squad, a cross-functional Data Engineering and Analytics Engineering team within the Platform Mission
• You’ll help build and maintain trusted analytical models, metrics, and data products that power developer productivity, platform health, and leadership decision-making
• Working closely with Data Engineers, Product, Engineering, and Platform partners, you’ll translate platform signals into reliable, well-modeled data assets that help Spotify ship faster and safer
• Build and maintain analytical data models using dbt (or similar SQL-based transformation frameworks) in BigQuery for a broad set of stakeholders
• Build and operate reliable data pipelines using SQL, with a focus on testing, observability, and CI/CD
• Help define and evolve key metrics for platform health, developer productivity, and ML/AI platform adoption
• Partner with Data Engineers on upstream pipelines and collaborate with Product, Engineering, and Data Science to scope and deliver insights
• Improve data quality, performance, and cost efficiency across pipelines and models, including troubleshooting and backfills
• Contribute to dashboards and self-serve data products that enable better decision-making across teams
• Follow and contribute to data quality, testing, and documentation practices across the analytics layer
• Participate in a fair support rotation for key datasets, pipelines, and analytical products
• We offer you the flexibility to work where you work best! There will be some in person meetings, but still allows for flexibility to work from home

Benefits

• Extensive learning opportunities, through our dedicated team, GreenHouse
• Flexible share incentives letting you choose how you share in our success
• Global parental leave, six months off - fully paid - for all new parents
• All The Feels, our employee assistance program and self-care hub
• Flexible public holidays, swap days off according to your values and beliefs