Senior Data Engineer

Triumph•San Francisco, CA

31d•Onsite

About The Position

Triumph makes mobile gaming more thrilling by letting players wager -- and win -- real money, play in mass multiplayer games, and compete in social tournaments. We've built the top app in our App Store category and sustained exponential month-over-month growth on our revenue and active players. We're hyper-scaling our team and continuing to innovate, launching new products like Rips, our collectibles app, which has found huge success and is continuing to expand. And we're just getting started. Triumph is backed by some of the top consumer VCs including Goodwater Capital, General Catalyst, and DraftKings Drive Fund. The Role Triumph is a skill-based real-money gaming platform in a period of rapid growth. Real money flows through every game, every matchmaking decision, and every payout, which means the data infrastructure powering those systems has to be fast, reliable, and built to scale. 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.

Requirements

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.

Nice To Haves

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.

Responsibilities

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.