We’re building the world’s most ubiquitous programming environment. We focus on inspiring creativity and generating value for creators through community, and we design simple yet scalable infrastructure primitives for the next generation of creators. We want to blur the line between learning and making– a place where you can hang out, tinker with ideas, learn new concepts, and launch a business all in the same day.

As a Data Engineer, your job is to facilitate data analytics and measurement at scale at Replit. You'll work with product and business teams to help build data pipelines and transformations to enable us to understand and measure product usage. You'll also work to make our data scientists and analysts -- and the business decisions that depend on them-- more powerful and efficient.

You will…:

• Build Spark pipelines using Databricks to help get product data into GCS and BQ from BigTable.
• Build Airflow pipelines leveraging data from Spark pipelines as well as client-instrumented events that will serve as the foundation for our metrics and product analytics
• Write UDFs to simplify analytics

Required skills and experience:

• 3+ years of industry experience in a Data Eng role
• Experience in B2B SaaS or consumer-facing products
• Experience building pipelines and workflows in Airflow and Spark/Databricks
• Interest in working closely with product teams
• Self-directed and comfortable working autonomously

Bonus Points: 

• Experience and opinions on dev tools
• You’ve worked in a growth-stage startup

Want to Learn More? 

• Replit Product
• Getting started with Replit 
• Repl from Repo
• Replit Multiplayer
• Interviewing + Culture
• My First Software Engineering Job Interview 
• Life at Replit 
• Operating Principles
• Reasons not to work at Replit

To achieve our mission of making programming more accessible around the world, we need our team to be representative of the world. We welcome your unique perspective and experiences in shaping this product. We encourage people from all kinds of backgrounds to apply, including and especially candidates from underrepresented and non-traditional backgrounds.