Senior Analytics Engineer
New
P
PlaybookFitness Tech
Europe (Remote)Full-TimeSenior
Salary not disclosed
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Job Details
- Languages
- English
- Experience
- 5+ years
- Required Skills
- SQLArtificial IntelligenceCI/CDBigQuerydbt
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
- 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.
- Product-minded engineering — you can design data that is consumed by end users in an application, not just by internal dashboards.
- 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.
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.
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