Senior Analytics Engineer

O projekcie: About Us We are Playbook, a leading creator platform for fitness, health, and wellness. Our mission is to help fitness creators build sustainable businesses, while enabling hundreds of thousands of users to live healthier lives. We are a fast-growing company in the fitness tech space, operating as a remote-first, product-driven team that values ownership, direct communication, and a strong growth mindset. We believe in "drivers, not passengers" — everyone is encouraged to take responsibility, think proactively, and act like an owner. Role Overview We're hiring our first dedicated data hire — a Senior Analytics Engineer with strong analytics chops who will own Playbook's data stack end-to-end. Today, our data lands in BigQuery via Hevo syncs that replicate our production systems 1:1 — raw, unstructured, and waiting to be turned into something useful. Your mission is to design and build Playbook's BigQuery data warehouse from scratch leveraging an ELT approach through Dataform or dbt, establish the conventions, testing, and CI/CD that will scale with us, and make that warehouse directly serve the business — starting with our Growth team. Beyond the warehouse, you'll be the go-to person for anything data-related across the company. You'll partner with our Growth team as their primary data counterpart — translating their questions into production-grade data models, codifying business metrics, and making data something the team can trust and move on. This is a high-ownership, high-autonomy role. You'll be the person the company turns to for anything data-related — from "how do we define MRR?" to "how do we attribute this signup to the right campaign?" to "why don't these two numbers match?". You'll set the bar for how data is built, trusted, and used at Playbook. What We Offer- Your work will directly affect creators and users on the platform. You'll work on features that ship quickly and matter.- We offer a wide compensation range to reflect different seniority profiles within this role. The upper end of the range is reserved for top candidates who demonstrate exceptional technical quality, product thinking, and ownership beyond day-to-day execution.- 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. Wymagania: 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.Nice to Have - Prior experience in a Data Engineering or BI Engineering role- 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 in 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. Codzienne zadania: - 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.