Data Engineer — Real-Time Analytics & Insights (SF)

Factory; San Francisco (CA); On-site; USD 120,000 - 180,000; Full time

Job summary

Factory is hiring a Data Engineer to own the data stack powering real-time insight across product, engineering, finance, and GTM. You’ll architect and evolve pipelines, models, and integrations that turn raw information into reliable decision-ready data.

You’ll build data models and warehouse architecture for analytics and reporting, design scalable pipelines with dbt, Airflow, or Dagster, and drive data quality, observability, governance, and a self-serve analytics culture.

Qualifications

• 4+ years of experience in data engineering or a full-stack data role with deep SQL and proficiency in Python or JavaScript.
• Hands-on data modeling, warehouse architecture, and BI-oriented schema design with dbt, Airflow or Dagster.
• Experience supporting or building BI environments.
• Strong statistical intuition and experimentation framework experience.

Responsibilities

• Build and refine data models and warehouse architecture for product analytics, usage reporting, and business operations.
• Design, operate, and scale pipelines that move, transform, and validate data across systems.
• Create integrations that bring third-party and operational data into a unified, trusted environment.
• Own the reporting surface from core datasets to leadership dashboards.
• Drive data quality, observability, governance, and documentation while enabling self-serve analytics.

Skills

SQL

Python

JavaScript

Data modeling

Warehouse architecture

BI tools

Experimentation

dbt

Airflow

Dagster

Job description

Factory is hiring a Data Engineer to own the data stack powering real-time insight across product, engineering, finance, and GTM. You’ll architect and evolve pipelines, models, and integrations that turn raw information into reliable decision-ready data.

You’ll build data models and warehouse architecture for analytics and reporting, design scalable pipelines with dbt, Airflow, or Dagster, and drive data quality, observability, governance, and a self-serve analytics culture.