1 day ago
Zürich, SwitzerlandMid Level / Senior
Responsibilities
- Design, build, and own core features, APIs, and business logic for a high-performance enterprise observability platform.
- Take systems from technical scoping and architecture through deployment while meeting enterprise reliability and performance standards.
- Collaborate with product, infrastructure, and data teams to deliver fast, actionable insights for engineering teams.
- Partner with SREs and customer-facing teams to diagnose and resolve production issues using AI-assisted workflows.
- Contribute through code review, mentorship, and architectural influence.
Requirements
- 2–5 years of professional software engineering experience focused on backend application systems or distributed platforms.
- Experience designing and shipping production systems at scale with high-throughput, low-latency, and enterprise reliability requirements.
- Strong proficiency in one or more server-side languages such as Go, Java, Scala, or Python.
- Ability to independently own complex technical problems from scoping through delivery.
- Experience collaborating across engineering, product, and infrastructure teams.
- Strong analytical thinking, structured problem-solving, and a record of shipping impactful customer-facing work.
- BS/MS in Computer Science, Engineering, or equivalent experience.
- Bonus: experience with observability platforms, telemetry pipelines, distributed tracing and logging systems, AI-assisted developer tools, LLM-integrated workflows, SRE practices, incident response automation, root-cause analysis tooling, or relevant open-source projects.
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.