5+ years of professional software engineering experience building and supporting large-scale, production distributed systems
Bachelor's or Master's degree in Computer Science, a related technical field, or equivalent practical experience
Strong fundamentals in distributed systems: consistency, fault tolerance, idempotency, exactly-once processing, queueing, and stream/batch data pipelines
Strong fundamentals in databases and data modeling — SQL fluency, schema design, performance tuning, and an appreciation for the operational realities of large-scale data systems
Proficiency in one or more of Java, Scala, Python, or Go, and the engineering practices that make systems easy to operate at scale (testing, observability, CI/CD, incremental rollout)
Demonstrated ability to independently design, spec, schedule, and deliver medium-sized projects with quality, and to contribute as a strong individual contributor on larger, multi-team efforts
A track record of owning reliability for what you build — participating in on-call, debugging production issues across unfamiliar areas, and driving systemic fixes rather than one-off patches
Excellent communication skills and a collaborative working style. You are comfortable operating across engineering disciplines and partnering with non-engineering stakeholders in Finance, Product, GTM, and Legal
Curiosity about and hands-on experience with AI as a builder's tool, and interest in shaping the commercial models that bring AI products to market
Prior experience in billing, payments, metering, revenue, or financial systems at a SaaS or cloud company
Experience with fraud detection, abuse mitigation, or trust/safety systems in a usage-based product
Experience designing or evolving pricing models for AI/ML products (inference, fine-tuning, agentic workloads)
Experience building on Snowflake or comparable cloud data platforms (Snowpark, Streams/Tasks, Iceberg, Streamlit, Cortex)
Contributions to internal AI developer-productivity tooling, agentic skills, or Cortex Code workflows