2 months ago
Base Salary
$236k - $339k/yr
Responsibilities
- Provide hands-on technical guidance, conduct code reviews, develop engineers, and establish engineering best practices.
- Design and implement highly available distributed platforms, pipelines, and data infrastructure components for global data processing.
- Lead cross-functional engineering projects from inception through implementation and production deployment across multiple quarters and teams.
- Partner with product managers, senior individual contributors, data science teams, and business units to deliver end-to-end data platform capabilities.
- Own or co-own reliability, observability, SLOs, capacity, and durable remediation goals for the assigned problem space.
- Contribute to team strategy, platform evolution, and responsible use of AI-assisted engineering.
Requirements
- 12+ years of software development experience in distributed systems, with a strong focus on data warehouse or data infrastructure engineering.
- Deep experience developing resilient, large-scale services in AWS, Azure, or GCP public-cloud environments.
- Demonstrated proficiency in distributed systems architecture and database fundamentals, including complex system design.
- Excellent technical communication and collaborative problem-solving skills across multidisciplinary teams.
- BS, MS, or PhD in Computer Science or a related technical field, or equivalent practical experience.
- Familiarity with data or ML orchestration systems across diverse architectures is a bonus.
Tech Stack
Categories
BackendData Engineering
About Snowflake
Snowflake builds a cloud-native data platform used by enterprises to store, integrate, share, and analyze data across AWS, Azure, and Google Cloud. Its core products span data warehousing, data lakes, data engineering, and governed data sharing, sold via consumption-based subscriptions. Founded in 2012 and publicly traded on the NYSE (SNOW) following a 2020 IPO, Snowflake supports analytics and data application workloads across industries.