Why This Job is Featured on The SaaS Jobs
This Associate Analytics Engineer role is notable in the SaaS ecosystem because it sits where product-scale data operations meet external accountability. In subscription platforms, Trust and Safety work increasingly depends on data that can stand up to scrutiny, and the emphasis here on auditability, documentation, and regulatory reporting reflects a maturing data function rather than ad hoc analytics.
For a SaaS career path, the work builds durable foundations in the modern data stack: modeling in dbt, designing reliable semantic layers, and putting validation frameworks around critical metrics. Just as importantly, it develops the habit of treating analytics outputs as governed assets, which translates across SaaS companies that need consistent definitions, reproducible reporting, and self-serve access for non-technical stakeholders.
This position suits early-career practitioners who want structured exposure to analytics engineering without being confined to dashboard production. It will appeal to candidates who enjoy careful problem framing, iterative improvement through reviews, and collaborating with legal, policy, and engineering partners where precision matters. It is a strong fit for someone motivated by measurable operational outcomes and clear data standards in a high-stakes domain.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
Spotify is investing in scalable, accountable data infrastructure to support transparency, regulatory compliance, and operational excellence across Trust & Safety (T&S). As an Associate Analytics Engineer, you will play a vital role in building and maintaining the data ecosystem that powers regulatory reporting and internal analytics. You will work at the intersection of data engineering, data science, and policy—transforming raw logs into reliable, accessible insights that protect our platform and its users.
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What you'll do
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Data Modeling & Maintenance: Assist in maintaining scalable, well-documented dbt models that support regulatory reporting and platform safety monitoring.
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Pipeline Optimization: Help build and optimize data pipelines in dbt, ensuring they meet the high standards of auditability required for compliance.
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Cross-Functional Support: Collaborate with legal, policy, and engineering teams to help translate complex transparency requirements into technical data solutions.
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Quality & Standards: Implement data definitions and documentation standards to improve data quality and support long-term maintainability of our safety metrics.
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Technical Reviews: Participate in structured reviews of existing analytics infrastructure to identify performance improvements and alignment with reporting needs.
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Self-Service Enablement: Support the development of our self-service ecosystem by contributing to intuitive semantic layers that serve a broad stakeholder base.
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Monitoring: Help implement validation frameworks that enable performance tracking and early detection of data quality issues.
Who you are
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Foundational Experience: 0–2 years of experience in analytics engineering or data engineering, with a strong desire to work in regulated, safety, or compliance-oriented environments.
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Technical Skills: Proficiency in SQL and Python, and familiarity with modern data stack tools (such as dbt, and control versioning)
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Modeling Interest: A passion for learning how to architect clean, testable data models that support complex reporting needs.
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Communication: Excellent communication skills, with the ability to learn how to translate technical details for legal and policy stakeholders.
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Detail-Oriented: You are able to validate assumptions and prioritize accuracy in a high-stakes environment.
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Mission-Driven: You have a passion for transparency, safety, and building data systems that serve social good.
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