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Mixpanel is a leading product intelligence and analytics platform trusted by 29,000+ companies including Workday, Pinterest, and Rakuten Viber. The Data Runtime team owns the data execution layer powering every Mixpanel product, ensuring customer queries run fast, cheap, and reliably at scale.
As a Software Engineer III on Data Runtime, you'll design and build the distributed systems that execute queries across Mixpanel's infrastructure. The team processes 500+ trillion events through their OLAP engine and manages blob storage systems handling 300 PiB/month at 1.2 Tbps sustained throughput globally.
You'll contribute to technical design for complex distributed-systems projects from prototype through global rollout, including elastic query compute, distributed file caching, columnar storage internals, and compaction strategies. You'll work alongside senior engineers (including Staff-level engineers with deep engine expertise), partner with peer infrastructure teams (Query Serving, Streaming, Data Foundation) and product teams on shared architecture, and influence the technical direction of the compute layer powering Mixpanel's AI-first future.
Key responsibilities include owning projects end-to-end—driving design, building, shipping, and operating in production. You'll demonstrate strong technical communication (especially written: design docs, code review, async discussion), sound judgment on technical tradeoffs informed by production experience, and high ownership and accountability.
Required: Bachelor's in Computer Science or equivalent; 3+ years building and operating data infrastructure/backend systems with a track record of shipping quality work quickly; solid foundation in distributed systems or data infrastructure; experience owning significant components end-to-end; strong async communication skills.
Valued technical skills: Go, C/C++, Python, SQL; distributed systems (sharding, replication, scheduling, consistent hashing); storage & query engines (columnar formats like Arrow/Parquet, indexing, compression); distributed caching (admission/eviction policies, tiered storage); performance engineering (profiling, concurrency, memory management); cloud infrastructure (GCP/AWS/Azure, Kubernetes); reliability practices (observability, SLOs, incident management); AI-augmented engineering tools.
