Spotify

Analytics Engineer II

Build trusted analytical models and data products powering platform insights at scale.

Captured June 2026Closed as of Oct 11, 2026 ↗

The role

Analytics Engineer II within Spotify's Platform Central Data squad, a cross-functional team responsible for infrastructure enabling rapid growth. The role focuses on constructing and maintaining analytical models, metrics, and self-serve data products that support developer productivity, platform health, and executive decision-making across the organization.

What you would do

  • Design and operate analytical data models using dbt or equivalent SQL transformation tools in BigQuery
  • Construct and maintain reliable data pipelines emphasizing testing, observability, and continuous integration
  • Define and refine key metrics tracking platform health, developer productivity, and AI/ML adoption
  • Collaborate with Data Engineers on upstream pipeline development and with Product and Data Science teams on insight delivery
  • Optimize data quality, performance, and cost efficiency across analytical systems
  • Develop dashboards and self-serve analytics products enabling informed decision-making
  • Establish and maintain data quality, testing, and documentation standards
  • Participate in on-call support rotation for critical datasets and analytical products

What they asked for

  • Minimum 2 years of analytics engineering, data engineering, or equivalent experience
  • Advanced SQL proficiency and data modeling expertise
  • Hands-on experience with dbt or comparable SQL-based transformation frameworks
  • Proficiency with cloud data warehouses including BigQuery, Snowflake, Redshift, or Databricks
  • Knowledge of workflow orchestration platforms like Airflow, Dagster, Prefect, or Flyte
  • Strong commitment to data quality, reliability, and comprehensive testing
  • Competency with BI and visualization tools such as Looker or Tableau
  • Clear communication ability across technical and non-technical audiences
  • Ability to prioritize work and deliver in fast-paced environments

Nice to have

  • Background with platform or developer productivity data
  • Experience with experimentation frameworks or metrics
  • Exposure to ML/AI platform metrics and measurement
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