Staff Engineer – Experimentation Team
Builds statistical engines and warehouse-native analysis pipelines for A/B testing and adaptive experimentation (contextual bandits). Requires 10+ years experience, deep applied statistics/ML, backend expertise in Go/Python, and warehouse integration across Snowflake, Databricks, etc.
About the job
Responsibilities
- Build the experimentation statistical engine — hypothesis testing, sequential analysis, variance reduction (CUPED, Winsorization), power analysis. Ensure statistical correctness across all experiment types.
- Design warehouse-native experimentation that runs analysis inside customer warehouses (Snowflake, Databricks, Redshift, BigQuery). Build modular, warehouse-agnostic abstractions for rapid new backend support.
- Lead adaptive experimentation — contextual bandit systems, Bayesian optimization, automated allocation beyond simple A/B tests.
- Drive the platform roadmap with product, design, and data science. Shape what we build, not just how.
- Collaborate cross-functionally with Warehouse Integrations, SDK, Platform, and Data Science teams.
- Mentor engineers and raise the team's bar for statistical rigor and system design.
- Own operational excellence — monitoring, observability, incident response, on-call. Robust telemetry and alerting.
Qualifications
- 10+ years building large-scale experimentation platforms, statistical analysis systems, or data-intensive backend services.
- Applied-statistics knowledge: hypothesis testing, sequential analysis, variance reduction (CUPED), power analysis, experiment design. Comfortable with frequentist vs. Bayesian trade-offs.
- Experience with adaptive experimentation ML — contextual bandits, Thompson sampling, Bayesian optimization, or RL-based allocation.
- Track record designing warehouse-agnostic systems across Snowflake, Databricks, Redshift, BigQuery, or similar.
- Expertise in Go, Python, or similar for backend services and statistical computation.
- Experience with event-driven architectures, data pipelines, and large-scale data processing.
- Cloud environments (AWS, GCP) with infrastructure-as-code.
- Technical leadership: setting direction, breaking down complex problems, influencing across teams.
- Ability to translate statistical concepts for product and engineering audiences.
Pay
Target pay ranges based on Geographic Zones for Level 5:
Zone 1 (San Francisco/Bay Area or NYC Metropolitan Area, Boston, Seattle): $214,800 - $295,350
Zone 2 (Irvine, LA, Monterey, Santa Barbara, Santa Rosa, Austin, Portland, Philadelphia, Chicago): $193,400 - $265,870
Zone 3 (All other US locations): $182,600 - $251,020
Exact compensation may vary based on skills, experience, and location. Includes RSUs, health, vision, dental insurance, and mental health benefits.
Skills
Go, Python, Snowflake, Databricks, Redshift, BigQuery, AWS, GCP, Hypothesis Testing, Sequential Analysis, Cuped, Contextual Bandits, Bayesian Optimization, Event-Driven Architectures, Data Pipelines
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