Senior Data Engineer
Indexed description
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.
What You'll Do
- 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 predictions back into production systems where they drive real decisions.
- Data quality and reliability. Build the testing, validation, and monitoring frameworks that let a small team trust the data at scale.
- Partner with DS and engineering. You'll sit between two teams that move fast and need different things from the data layer. Translate between them and make both more productive.
- 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.
- Experience with streaming/real-time data systems (Kafka, Pub/Sub, Flink, or similar).
- Familiarity with analytics engineering and the modern data stack (Fivetran, Statsig, or similar tools).
- Exposure to quantitative or financial data environments where correctness and latency matter.
- Experience being an early or first data engineering hire. You've built from zero before and know what to prioritize.
Why Triumph?
- High growth. Build a high-scale consumer platform that touches gaming, finance, and social with the autonomy to set our web direction.
- High agency. Small, high-impact engineering team that is growing rapidly with significant opportunity for leadership and growth.
- High energy. Passionate team who are proud of our work and velocity (16x year over year growth).
- Competitive salary and benefits. $400/mo lunch credit, healthcare, vision, dental, 401k, etc.
Compensation Range: $200K - $400K
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