San Francisco, CA / Minneapolis, MN (Opportunity for Working Remotely in U.S.)
We’re an early stage, well-funded startup team with a proven track record of shipping open source software with global adoption. We put a premium on respectful, clear, and complete communication, and we expect each other to be creative, curious, effective, and empathetic.
We believe deeply that the right tools and abstractions enable not just technological transformation, but also organizational transformation. We strive to put the user and their hard work at the center of our decision making. In practice, that means we are looking for engineers who want to write clean APIs and helpful error messages, and who always try to understand user needs when designing a new system.
Our first product at Elementl is Dagster, an open-source Python library for building ETL pipelines, ML training, data integrations, and similar systems. Collectively, we describe these as a single category: data applications. Data applications are the true core of AI and ML training systems, and our goal is to make Dagster the de facto standard for structuring these systems.
You have lived our users' pain and believe that with the right systems, a fundamentally new and better path is possible. You will identify the tools and abstractions we need to build to bring that world into being and to make Dagster the obvious choice for data engineering. You have a passion for working in and building a fast-growing company from the ground up and are excited about working in a growing open-source community.
Shape the basic abstractions underlying the next generation of open-source data application tooling.
Independently drive engineering projects from design to completion.
Solve difficult technical problems throughout the software stack and get projects over the finish line, across front-end, back-end, or infrastructure.
Work collaboratively with the rest of the team to plan and execute on engineering projects.
If you don't think you meet all of the criteria below but still are interested in the job, please apply. Nobody checks every box, and we're looking for someone excited to join the team.
A bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field, or have had equivalent training or work experience
Experience building big data infrastructure using tools like Spark/EMR, Redshift, BigQuery, and Kafka is preferred
2+ years of relevant software development experience
Experience in a high-functioning engineering organization working on large-scale distributed systems
Strong command of computer science fundamentals like data structures and algorithms
Ability to participate in technical architecture discussions and help the team make key difficult technical decisions
Effective and cogent writing and speech
Excited about discussing difficult design questions with your colleagues
You belong here
We are committed to building an inclusive team and an open source community where no one feels out of place. We know that teams with diverse backgrounds state their assumptions more explicitly, think more rigorously, and build better software. Plus it's more fun and interesting to work with a wide variety of perspectives.
You should apply to work at Elementl if you want to work in, and help to build and strengthen, a high-performing software development environment where people of all backgrounds are welcome.
New to Data?
No problem. We believe that diverse experiences and backgrounds are an asset, enabling us to bring fresh perspectives to the problem space. Some of us are steeped in the data domain; some of us are not. It is a way of structurally having a “beginner’s mindset”, examining problems from first principles. Meanwhile, the experience on the team keeps us grounded in the reality of the current state of the ecosystem and the practical problems on the ground.
Dagster is built in Python and TypeScript to work on macOS, Posix, and Windows. We use GraphQL, Apollo, and React to develop beautiful frontend tooling. We integrate with a wide range of databases, data warehouses, orchestration engines, compute substrates, and cloud services.