Software Engineer - PlanetScale Vitess
Builds and operates scalable backend database infrastructure for PlanetScale’s Vitess platform, solving distributed-systems and performance challenges at scale. Requires 5+ years of backend software engineering experience, strong Go proficiency, and expertise with databases and Kubernetes.
About the job
Responsibilities
- Design and build critical systems powering PlanetScale's database platform, serving millions of queries per second.
- Collaborate with expert engineers to solve complex distributed-systems challenges.
- Independently solve engineering and business problems with high autonomy.
- Work directly with customers to understand their needs and translate them into robust technical solutions.
- Build high-quality, scalable systems with clean, maintainable code.
- Contribute to complex codebases and debug issues across multiple systems.
- Mentor junior engineers.
Requirements
- 5+ years of software engineering experience focused on backend systems.
- Strong proficiency in Go, with experience in Python, Java, or C++.
- Experience with MySQL or other relational databases.
- Working knowledge of Kubernetes and containerized applications.
- Experience building and operating distributed systems at scale.
- Strong experience with distributed systems, databases, and performance optimization.
- Ability to work independently, make sound technical decisions, and collaborate effectively.
Nice-to-haves
- Experience with database internals, query optimization, or distributed consensus algorithms.
- Contributions to open-source projects, especially in database or infrastructure projects.
- Experience with cloud platforms such as AWS, Google Cloud, or Azure.
- Knowledge of monitoring, observability, and debugging tools.
- Experience at a high-growth technology company.
Compensation and Benefits
- Base salary range: $120,000–$290,000 USD.
- Total compensation may include base salary, variable compensation where appropriate, benefits, and equity.
Skills
Go, Python, Java, C++, MySQL, Relational Databases, Kubernetes, Docker, Distributed Systems, Database Internals, Query Optimization, AWS, GCP, Azure, Observability
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