Senior Data Engineer

triumph-arcade; San Francisco (CA); On-site; USD 180,000 - 240,000; Full time

Benefits offered by this job

Lunch credit

Healthcare

Vision

Dental

401k

Job summary

Triumph Arcade in San Francisco seeks a first dedicated data engineer to own the entire data stack—from ingestion to production integration. You’ll work between a quantitative strategy team and a fast-moving engineering org, building the foundation that every model and decision relies on.

You’ll architect the data warehouse in BigQuery, own dbt models, orchestrate robust pipelines, and design streaming and reverse-ETL capabilities so model outputs drive real decisions in production.

Qualifications

• Strong software engineering fundamentals and well-tested code.
• Deep experience with SQL and Python in production data contexts.
• Hands-on data warehousing experience (BigQuery/Snowflake/Redshift) and dbt.
• Experience with orchestration tooling (Airflow/Dagster/Prefect).
• Understanding of dimensional modeling and slowly changing dimensions.
• Ability to work independently and make sound architectural decisions.

Responsibilities

• Architect and own the data warehouse in BigQuery for performance and scale.
• Build and maintain transformation layers with dbt end-to-end.
• Design robust data pipelines with orchestration, monitoring and recovery.
• Design and implement streaming real-time data systems.
• Close loop between model outputs and production systems with reverse ETL.
• Build testing, validation, and monitoring frameworks to ensure data reliability.
• Translate needs between data science and engineering teams.

Skills

SQL

Python

Data warehousing

dbt

Airflow

Dagster

Architectural decisions

Tools

BigQuery

Snowflake

Redshift

dbt

Airflow

Dagster

Prefect

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.