Mixpanel
Software Engineer, Data Runtime
About this role
Join Mixpanel's Data Runtime team as a Software Engineer III to design and build the distributed systems executing queries across a massive OLAP engine processing 500+ trillion events. You'll own complex infrastructure projects—from elastic compute to distributed caching—working alongside senior engineers to power AI-first analytics at global scale.
What you'll do
- Design and implement distributed systems for query execution across Mixpanel's OLAP engine handling 500+ trillion events
- Own end-to-end technical design for major projects including elastic query compute, distributed file caching, and columnar storage
- Partner with infrastructure teams (Query Serving, Streaming, Data Foundation) on shared architecture decisions
- Drive performance optimization, profiling, and benchmarking to ensure queries run fast and cost-efficiently at any scale
- Contribute to reliability improvements including observability, SLO management, and incident response
- Write technical design documents and participate in code reviews across a distributed team
What they're looking for
- Go, C/C++, Python, and SQL
- Distributed systems (sharding, replication, scheduling, consistent hashing)
- Columnar storage formats (Parquet, Arrow) and query engine internals
- Distributed caching, admission/eviction policies, and tiered storage
- Performance engineering and profiling tools (pprof, perf)
- GCP (GCS, GKE, Spanner), Kubernetes, or equivalent cloud infrastructure
- Observability, metrics, tracing, and SLO-driven reliability
- LLM-augmented development and operations
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Mixpanel
Mixpanel builds data analytics and insights platforms that help companies understand user behavior and make data-driven decisions. The company is hiring Software Engineers for teams focused on AI-powered automated insights and developer infrastructure to enhance both the analytics experience and internal engineering workflows.
- Website
- mixpanel.com
Likely interview questions
- Describe a complex distributed system project you owned end-to-end—how did you approach the design and what tradeoffs did you make?
- Tell us about your experience optimizing query performance or data infrastructure for scale; what were the bottlenecks you encountered?