Playbook - Senior Analytics Engineer
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Requirements
• 5+ years of experience in data engineering, analytics engineering, or a hybrid role — with a track record of owning a data warehouse in a production environment. • Expert-level SQL and deep experience with BigQuery (or a comparable cloud warehouse: Snowflake, Redshift, Databricks). • Hands-on experience with Dataform or dbt — building modular, tested, documented ELT pipelines and enforcing conventions across a codebase. • Strong grasp of dimensional modeling — facts, dimensions, slowly changing dimensions, incremental models, and knowing when to denormalize vs. normalize. • Fluent with CI/CD for data — Git workflows, environment separation (dev/staging/prod), automated tests, and deployment pipelines for warehouse code. • Experience with a managed ingestion tool like Hevo (what we use today) or similar — and a solid intuition for when these tools are enough vs. when to build custom. • Hands-on experience with Metabase — including an understanding of how its capabilities and quirks shape warehouse design decisions. Familiarity with other BI tools (Tableau, Power BI, AWS QuickSight) is a plus. • Product-minded engineering — you can design data that is consumed by end users in an application, not just by internal dashboards. You think about performance, API shape, and data contracts. • Experience working with LLMs / AI in data workflows — using AI to accelerate modeling, documentation, SQL generation, or building natural-language interfaces on top of the warehouse. • Excellent communication in English — you can explain technical trade-offs to non-technical stakeholders and partner with Growth, Product, and Engineering on equal footing. • Ownership mindset — comfortable being the first data person, making decisions with incomplete information, and being accountable for outcomes, not just tickets. • Prior experience in an Analytics Engineering or BI Engineering role — sitting at the intersection of data engineering and business. • Strong understanding of SaaS and subscription business models — you're fluent in MRR, ARR, churn, deferred revenue, LTV, and CAC, and you know where the tricky edge cases live (refunds, coupons, trials, annual plans, revenue recognition). • Experience at a creator economy, marketplace, or subscription platform. • Experience building or integrating experimentation / A/B testing infrastructure — exposure assignment, metric computation, stats pipelines. • Experience with Python for ad-hoc data work, custom ingestion scripts, or orchestration.
Responsibilities
• Design and build Playbook's data warehouse from the ground up in Dataform or dbt on BigQuery — defining our raw/staging/intermediate/marts architecture, modeling conventions, naming, and testing standards. • Own our ingestion layer — manage and extend our Hevo setup across Stripe, production Postgres (AWS), Mixpanel, GA4, HubSpot, Meta Ads, Google Ads, Ahrefs, PostHog, and new sources as they come. • Establish CI/CD, testing, and data quality practices for the warehouse — environments, automated tests, lineage, freshness checks, and alerting so we can trust what we ship. • Be the Growth team's data partner — turn their questions into production-grade data models, define and codify business metrics (MRR, churn, LTV, CAC, activation, retention, attribution), and make self-serve analytics actually self-serve. • Own, build, and evolve Playbook's creator-facing analytics product — the data layer that powers the metrics and insights creators see inside the platform about their own business performance. • Support product and engineering teams on data-heavy features — partner on data models, pipelines, and metric definitions for features that rely on the warehouse. • Own data requests across the company — triage, prioritize, and either solve them directly or invest in the models that unblock them at scale. • Maintain and evolve our BI layer — making sure dashboards and reports are trustworthy, documented, and built on top of our modeled layer rather than raw tables. • Set the direction for Playbook's data platform — what to build vs. buy, where to invest, and how the stack should evolve as we grow.
Benefits
• Your work will directly affect creators and users on the platform. You'll work on features that ship quickly and matter. • Equity options. • 100% remote with flexible working hours and async-friendly culture. Collaboration across Europe and the US East Coast. • A collaborative team that values ownership, open communication, and autonomy over micromanagement. • Yearly team retreats focused on connection, alignment, and building strong team relationships. • Paid Time Off.
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