Job description
Engineering Manager Performance
Company Overview
Snowflake is about empowering enterprises to achieve their full potential — and people too. With a culture that’s all in on impact, innovation, and collaboration, Snowflake is the sweet spot for building big, moving fast, and taking technology — and careers — to the next level. Snowflake is proud to be the Official Data Collaboration Provider for LA28 and Team USA. Snowflake delivers the AI Data Cloud — a global network where thousands of organizations mobilize data with near-unlimited scale, concurrency, and performance. Inside the AI Data Cloud, organizations unite their siloed data, easily discover and securely share governed data, and execute diverse analytic workloads. Wherever data or users live, Snowflake delivers a single and seamless experience across multiple public clouds. Snowflake’s platform is the engine that powers and provides access to the AI Data Cloud, creating a solution for data warehousing, data lakes, data engineering, data science, data application development, and data sharing. Join Snowflake customers, partners, and data providers already taking their businesses to new frontiers in the AI Data Cloud.
Job Summary
Drive the Future of the Data Cloud Snowflake is redefining how the world mobilizes data. To sustain our rapid growth, we need infrastructure that is as intelligent and efficient as the platform we deliver to customers. Within our Platform & Infrastructure Engineering organization, the Capacity, Performance & Efficiency (CPE) team ensures Snowflake’s compute and storage layers run at peak performance—maximizing reliability, throughput, and cost efficiency across all major clouds. We’re looking for an Engineering Manager to lead the CPE Performance team in Berlin. This group builds systems that measure, model, and optimize the performance of Snowflake’s distributed infrastructure—spanning CPU, memory, and storage hardware across AWS, Azure, and GCP. Your work directly influences how Snowflake scales globally and how efficiently our workloads run on next-generation hardware.
Key Responsibilities
- Lead Engineering Execution: Guide a team building software that continuously measures and improves system performance across multi-cloud fleets.
- Develop distributed performance frameworks and profilers that operate at hyperscale.
- Collaborate on adaptive scheduling, placement, and resource management systems that improve efficiency and predictability.
- Partner with capacity and hardware engineering to validate new instance types, GPUs, and compute generations for optimal performance.
- Drive Technical Excellence: Shape the roadmap for benchmarking, telemetry, and performance regression detection.
- Translate hardware and kernel-level insights into distributed-system optimizations.
- Contribute to the design of scalable services that keep Snowflake performant as we adopt new processor generations, accelerators, and storage technologies.
- Grow & Empower the Team Mentor engineers in building high-impact systems and developing a strong performance-engineering culture.
- Foster collaboration and curiosity around low-level system design and large-scale distributed architectures.
- Help shape engineering practices in Berlin, building a team known for precision, speed, and impact.
- Collaborate Across Snowflake: Work with product, infrastructure, and finance teams to optimize cost and utilization.
- Engage directly with cloud providers to evaluate hardware roadmaps and performance characteristics.
- Communicate key insights and results to leadership and cross-functional stakeholders.
Requirements / Qualifications
- 8+ years of hands-on software engineering experience, including 2+ years managing engineers.
- Proven background in distributed systems, large-scale infrastructure, or performance engineering.
- Deep understanding of how modern cloud hardware (CPU generations, GPUs, accelerators, networking, storage) affects system performance.
- Experience with performance analysis tools, telemetry systems, and scalable monitoring frameworks.
- Proficiency across multiple cloud environments (AWS, Azure, GCP).
- Strong coding background (Python, C++, Java, or Go).
- Fluency in distributed systems concepts—scheduling, orchestration, data locality, and resource isolation.
- Ability to debug complex interactions between software and hardware.
- Excellent communication and leadership skills; capable of guiding both technical direction and people growth.
Preferred Qualifications
- Experience with workload benchmarking, fleet modeling, or predictive performance analysis.
- Exposure to large-scale compute scheduling systems (Kubernetes, Borg, Mesos, Volcano, Kueue, etc.).
- Familiarity with modern processor and GPU architectures and their performance profiles.
- Contributions to open-source infrastructure or performance-engineering tools.
Location
Berlin, Berlin, Germany, Remote: False
