Our mission is to automate coding. The first step in our journey is to build the best tool for professional programmers, using a combination of inventive research, design, and engineering. Our organization is very flat, and our team is small and talent dense. We particularly like people who are truth-seeking, passionate, and creative. We enjoy spirited debate, crazy ideas, and shipping code.
About the Role
As one of Cursor’s first Analytics Engineers, you’ll work hands-on across the entire stack to build data products and drive strategic decisions across product, GTM, and research. You'll partner directly with founders and area leads on critical questions, collaborating with uniquely data-savvy stakeholders who are eager to jump into SQL and dbt. Through this collaboration, you’ll pioneer the next frontier of data: defining how Cursor itself transforms data science by building a data stack around Cursor Agent for self-serve analytics.
- Read our blogpost on measuring the impact of Semantic Search: https://cursor.com/blog/semsearch
What you’ll do
- Partner with area leads in Finance, Growth, Product, and Agent Quality to understand their data needs and build foundational datasets.
- Up-level our data stack by evaluating new tooling and AI integrations, while partnering with Data Infra and product engineers to maximize the impact of existing tooling.
- Ensure the quality and reliability of data in our warehouse.
- Help guide a vibrant self-serve data culture to make self-serve insights accessible and trustworthy.
- Establish data culture and foundations as an early member of the data team and our first analytics engineer.
You may be a fit if
- You have at least 4+ years of full-time analytics engineering experience.
- You’ve been an early data member at a hyper-growth startup or research org. You know how to scale data from 10 to 50 data scientists.
- You’ve optimized queries for speed and cost on datasets that grow by billions of rows per day.
- You can write SQL and Python in your sleep.
- You care deeply about accuracy and detail.
- You’re excited about the modern data stack and self-serve data.
- You’re excited to build data products end to end, even if it requires going outside the original job description.
- #LI-DNI