Spotify
Platform
AnalyticsEngineerII
Neural analysis suggests this role is
optimal for Mid candidates.
“Analytics Engineer II at Spotify. Skills: analytics engineering, data engineering, dbt, SQL, cloud data warehouse, data modelling, BI/visualisation tools. 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”
What You'll Achieve.
enable Spotify to learn quickly and scale easily; welcome a billion customers; build and maintain trusted analytical models, metrics, and data products that power developer productivity, platform health, and leadership decision-making; translate platform signals into reliable, well-modeled data assets that help Spotify ship faster and safer; enable better decision-making across teams
Industry & Context.
troubleshooting
What They're Looking For.
Must Have
2+ years of experience in analytics engineering, data engineering, or a related field, SQL skills, experience with data modelling, experience with dbt (or similar SQL-based transformation frameworks), experience with a cloud data warehouse such as BigQuery, Snowflake, Redshift, or Databricks SQL, familiar with workflow orchestration tools such as Airflow, Dagster, Prefect, or Flyte, care about data quality, reliability, and testability, comfortable working with BI/visualisation tools such as Looker or Tableau, communicate clearly with both technical and non-technical partners, able to prioritize and deliver in a fast-moving environment
Nice to Have
experience with platform or developer productivity data, experimentation, or ML/AI metrics
What You'll Do.
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
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
and Data Science to scope and deliver insights
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
and documentation practices across the analytics layer
Participate in a fair support rotation for key datasets
and analytical products
How You'll Work.
Team & Collaboration
Working closely with Data Engineers, Product, Engineering, and Platform partners; Partner with Data Engineers on upstream pipelines and collaborate with Product, Engineering, and Data Science to scope and deliver insights; communicate clearly with both technical and non-technical partners
Communication Scope
communicate clearly with both technical and non-technical partners
Process & Methodology
able to prioritize and deliver in a fast-moving environment
Full Job Description
## Description 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. ## What You"ll Do 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 ## 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
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