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Analytics Engineer

Spotify LogoSpotify
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Salary

$125,562 - $179,374

Location

New York City OR Stockholm. 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.

We’re looking for an Analytics Engineer with a deep SQL/analytics toolkit to shape the analytics data layer that powers our Data Science environment. You’ll join a tight‑knit crew of analytics and data engineers, building robust, scalable models and pipelines in BigQuery and dbt across a huge and diverse data landscape. You’ll partner closely with Data Scientists, Data Engineers, and product teams to define clean data contracts, standardize metrics, and make trusted data easy to find and use.

Join the band that turns billions of events into insight.

What You'll Do

  • Be a primary contributor to the analytics data layer — designing, modeling, and maintaining datasets that surface critical signals from massive, heterogeneous sources.
  • Work hand‑in‑hand with Data Scientists to design schemas, features, and pipelines that unlock exploratory research and repeatable analysis.
  • Engineer novel datasets and features, and elevate existing ones, to enable flexible analysis of subscription and consumption dynamics across our platform.
  • Implement and evolve ETL/ELT pipelines in dbt and BigQuery; collaborate with the engineering team on data infrastructure improvements as needed.
  • Establish and champion reporting best practices and a standardized metrics layer for the Business Strategy & Insights Org. — driving consistency, documentation, and discoverability.
  • Raise the bar on analytics engineering: code review, testing, data quality checks, performance optimization, observability, and CI/CD for analytics code.
  • Support downstream users — debugging queries, optimizing models, and sharing context — while mentoring teammates and uplifting standards.

Who You Are

  • 2+ years of professional experience analyzing complex data with SQL and Python.
  • A demonstrable track record of contributing to analytics solutions for Data Science teams; you turn open-ended goals into scoped, actionable plans.
  • Significant ETL/ELT experience with very large and complex datasets, including managing DAG dependencies (e.g., Airflow, Dagster, or similar).
  • Deep competency with SQL on distributed/data-lake systems (e.g., BigQuery, Presto, Spark/Hive SQL), including nested data handling, window functions, query tuning, and partitioning strategies.
  • Collaborative, low-ego, and invested in helping peers level up. You care about reliable pipelines, clear models, and measurable impact.

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