Premier consumer entertainment and commerce. Backed by General Catalyst.
About the role
As our first dedicated data engineering hire, you'll own the full data stack: ingestion, transformation, warehouse architecture, pipeline reliability, and the systems that connect model outputs back to production. You'll work at the intersection of a quantitative strategy team and a fast-moving engineering org, building the foundation that both depend on. Architect and own the data warehouse. Design and optimize our BigQuery environment for performance, cost, and reliability as data volumes scale with user growth.
What they're looking for
- Strong software engineering fundamentals. You write clean, maintainable, well-tested code
- Deep experience with SQL and Python in production data contexts
- Hands-on experience with data warehousing (BigQuery, Snowflake, Redshift, or similar) and transformation frameworks (dbt strongly preferred)
- Experience building and operating data pipelines with orchestration tooling (Airflow, Dagster, Prefect, or similar)
- Understanding of data modeling patterns (dimensional modeling, slowly changing dimensions, incremental materialization)
- Ability to work independently and make sound architectural decisions. You'll have a lot of autonomy and you need to use it well
More about this role
As our first dedicated data engineering hire, you'll own the full data stack: ingestion, transformation, warehouse architecture, pipeline reliability, and the systems that connect model outputs back to production. You'll work at the intersection of a quantitative strategy team and a fast-moving engineering org, building the foundation that both depend on.
Architect and own the data warehouse. Design and optimize our BigQuery environment for performance, cost, and reliability as data volumes scale with user growth.
Build and maintain transformation layers. Own our dbt project end-to-end, including models, testing, documentation, and CI/CD, turning raw event streams into clean, trusted datasets.
Pipeline orchestration. Build and manage robust data pipelines with proper orchestration, monitoring, alerting, and failure recovery. Nothing should break silently.
Real-time data systems. Design and implement streaming infrastructure for use cases where batch processing falls short: live game economics, real-time risk signals, and in session player behavior.
Reverse ETL and production integration. Close the loop between model outputs and the product by getting scores, segments, and...
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