Data Engineer

Location: South Market, San Francisco

Pay: 150K-200K

*Unable to provide sponsorship at this time*

A fast‑growing consumer technology startup is hiring its first dedicated Data Engineer to build and own the full data infrastructure that powers real‑time decisioning, product features, analytics, and revenue-critical systems. This is a high‑impact role where you will architect the entire data stack end‑to‑end, partnering closely with both engineering and quantitative teams in a fast‑moving environment.

You will design the warehouse architecture, own transformation layers, build reliable pipelines, and develop the real‑time systems that enable the company to scale. This is a rare opportunity to define standards, tooling, quality frameworks, and system design from the ground up.

• Architect and manage the data warehouse: Design and optimize the company's warehouse environment for performance, reliability, and cost efficiency as data volumes grow.
• Own the transformation layer: Build and maintain the core transformation framework (e.g., dbt), including models, documentation, testing, and CI/CD.
• Build and operate data pipelines: Develop robust, well‑monitored data pipelines with modern orchestration tools, ensuring failures are detected and handled reliably.
• Develop real‑time data systems: Design streaming infrastructure for use cases where low latency matters, such as live user behavior, in‑session signals, and dynamic business logic.
• Integrate data into production systems: Implement reverse ETL and other mechanisms to ensure model outputs and derived data reach production systems where they drive real‑time decisions.
• Establish data quality frameworks: Build testing, monitoring, and validation systems to ensure accuracy, trust, and reliability in a rapidly scaling environment.
• Collaborate cross‑functionally: Work closely with both data science/quantitative teams and software engineering teams, enabling fast iteration and data‑driven decision making across the organization.
  
  

• Strong software engineering fundamentals with clean, maintainable, well‑tested code.
• Deep experience with SQL and Python in production environments.
• Hands‑on experience with major data warehouse technologies (e.g., BigQuery, Snowflake, or Redshift).
• Experience with transformation tooling such as dbt
• Experience building and operating pipelines using modern orchestration tools (e.g., Airflow, Dagster, Prefect).
• Understanding of data modeling approaches (dimensional modeling, SCDs, incremental models).
• Ability to work autonomously and make strong architectural decisions in a high‑ownership environment.
  
  

• Exposure to streaming and real‑time systems (Kafka, Pub/Sub, Flink, etc.).
• Familiarity with modern data stack tooling (e.g., Fivetran, analytics engineering best practices).
• Experience working in high‑accuracy, high‑throughput domains such as financial, quantitative, or real‑time decisioning environments.
• Background as an early or founding data engineering hire.