Software Engineer, Data Runtime

About Mixpanel

Mixpanel is the leading product intelligence and analytics platform, trusted by more than 29,000 companies to help understand how people use the products they build. By combining powerful analytics with AI that knows your business, Mixpanel helps teams see what's working, diagnose what's not, and decide what to build next. Learn more at  .

Mixpanel turns data clarity into innovation. Trusted by more than 29,000 companies, including Workday, Pinterest, LG, and Rakuten Viber, Mixpanel's AI-first digital analytics help teams accelerate adoption, improve retention, and ship with confidence. Powering this is an industry-leading platform that combines product and web analytics, session replay, experimentation, feature flags, and metric trees. Mixpanel delivers insights that customers trust. Visit  to learn more.

Mixpanel Engineering is a small, fast-moving team focused on delivering real value to customers. We build powerful AI-powered product analytics while obsessing over clarity, simplicity, and delight. Engineers here own problems end to end. You can move across the stack to ship impact without being blocked by silos or heavy process. Product innovation drives our business, and product engineering teams own that responsibility.

Our OLAP engine queries over 500 trillion events; a typical blob storage system we interact with processes 300 PiB/month at 1.2 Tbps sustained, and we run many of them across the world.

The Data Runtime team owns the data execution layer that powers every Mixpanel product. We ensure that every customer query runs fast, cheap, and reliably, at any scale.

This is an exciting time to join. Mixpanel's agentic and AI-first products are driving rapid growth in query volume, and Data Runtime is making the big bets that power it. We're investing in elastic query compute and a distributed file cache that will let us scale query workloads dramatically without scaling cost with them. We sit at the center of the data platform, interfacing daily with Query Serving, Streaming, Core Analysis, Data Foundation, Product, and other teams across the company.

The team combines deep expertise in high-scale distributed systems and backend engineering. It's a senior-heavy team, including Staff-level engineers with years of context in the engine internals, which means you'll be surrounded by people you can learn from and grow with.

As a Software Engineer III (L3) on Data Runtime, you'll design and build the systems that execute every query 'll contribute to the technical design for complex distributed-systems projects, from prototype to global rollout, on initiatives like elastic query compute, the distributed file cache, columnar storage internals, and compaction.

You’ll work alongside some of the most senior engineers at Mixpanel, partner with peer infrastructure teams (Query Serving, Streaming, Data Foundation) and product teams (Core Analysis, AI-powered experiences) on shared architecture, and have real influence over the technical direction of the compute layer that Mixpanel's AI-first future runs on.

• Bachelor's degree in Computer Science, a related field, or equivalent practical experience.
• 3+ years of professional software engineering experience building and operating data infra/backend systems, with a track record of shipping quality work quickly.
• A solid foundation in distributed systems or data infrastructure, and the drive to go deep.
• Experience owning a project or significant component end to end: driving the design, building it, shipping it, and running it in production.
• Strong technical communication, especially in writing, e.g., design docs, code review, async discussion across a distributed team.
• Sound judgment in technical tradeoffs, informed by real lessons from operating systems in production.
• High ownership and accountability.
• A desire to be at the forefront of the infrastructure powering AI-first analytics at thousands of companies.

• Languages:
  
  Go, C/C++, Python, and SQL.
• Distributed systems: sharding/partitioning, replication, distributed scheduling, work fanout/merge, consistent hashing.
• Storage & query engines: columnar formats (Arrow/Parquet or in-house equivalents), indexing, and compression.
• Caching: distributed file/block caches, admission and eviction policies, tiered storage.
• Performance engineering: profiling (pprof, perf), concurrency, memory management, benchmarking.
• Cloud & infra: GCP (GCS, GKE, Spanner) or equivalent AWS/Azure, Kubernetes.
• Reliability: observability (metrics, tracing, logging), SLOs, incident management.
• AI-augmented engineering: LLM tooling to accelerate development, triage, and operations.

Teams operate as autonomous pods, with engineering, product, and design working side by side on clear strategic goals. Teams have direct access to customer feedback, prod