Job description

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. 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. QUALIFICATIONS - 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. PREFERRED - 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 THIS ROLE You'd be building and owning the entire data engineering function at a hypergrowth consumer startup where data runs through every layer of the business. Every product decision, every dollar of revenue, and every player interaction flows through the stack you'll build. You'll set the architecture, choose the tooling, define the standards, and see your work become load-bearing infrastructure from day one. If you want to build something from scratch at a company that lives and breathes data, this is a rare opportunity 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. Our team gathers 5 days a week at Triumph’s headquarters at Levi’s Plaza in San Francisco.

Requirements

Must-haves

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.
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.
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).
Our team gathers 5 days a week at Triumph’s headquarters at Levi’s Plaza in San Francisco.

Nice-to-haves

Hands-on experience with data warehousing (BigQuery, Snowflake, Redshift, or similar) and transformation frameworks (dbt strongly preferred).
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.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

ci/cdsqlpython

Platform: ashby
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401k