We are looking for a highly skilled Data / Analytics Engineer to take ownership of our analytics layer across a modern DTC and subscription-based business.
This role sits at the intersection of data engineering, analytics, and business decision-making. You will be responsible for ensuring data accuracy, defining key metrics, and translating complex datasets into actionable insights used across product, marketing, and leadership.
Key Responsibilities * Design, build, and maintain scalable data models across multiple sources (Shopify, Stripe, App Store, Amazon, etc.) * Develop and optimize SQL queries (CTEs, window functions, multi-source joins, deduplication) * Build and maintain dashboards (Grafana or similar) for real-time and operational monitoring * Define and standardize key business metrics (LTV, cohorts, churn, ARPU, refill rates) * Ensure data consistency across systems, including handling returns, fulfillment lag, and edge cases * Work with ELT pipelines (e.g. Fivetran) and understand data ingestion and transformation flows * Collaborate with product, marketing, and leadership teams to translate data into decisions * Support investor/board-level reporting and be able to clearly explain and defend metric definitions
Requirements * Strong SQL skills (advanced queries, performance optimization) * Experience with data warehouses (Redshift, Snowflake, BigQuery, or similar) * Experience building dashboards (Grafana, Tableau, Looker, or similar) * Familiarity with Shopify, subscription models (Recharge), and eCommerce data structures * Solid understanding of DTC metrics (LTV, retention, churn, cohort analysis) * Experience working with ELT tools (Fivetran or similar) * Strong analytical thinking and ability to work with imperfect data * Ability to communicate insights clearly to both technical and non-technical stakeholders
Nice to Have * Experience with subscription businesses or recurring revenue models * Background in product analytics or growth analytics * Experience with event tracking tools (Segment or similar) * Experience with Python or data transformation frameworks (dbt, etc.)
What We’re Looking For
We are not just looking for someone who can build dashboards — we are looking for someone who can own the metric layer.
You should be comfortable answering questions like: * How should LTV be defined for this business? * What is the correct way to count a “customer”? * How do returns and fulfillment delays affect revenue reporting?
This role requires both technical depth and strong business judgment.
