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Amplitude is seeking an Engineering Manager to lead the Data Infrastructure team within Statsig Experiment, the company's experimentation platform. This role manages a multidisciplinary team of 10-15 software engineers, data engineers, and data scientists responsible for systems powering experimentation at scale.
The team owns three critical areas: (1) Data ingestion—collecting and importing experiment exposures, custom events, OpenTelemetry data, and real user monitoring data across SDKs, streaming systems, cloud storage, and customer data warehouses; (2) Data computation—building distributed computation systems that transform raw data into accurate, timely experiment results; and (3) Stats engine—developing and productionizing statistical methods that help customers make trustworthy decisions from experiments.
This is not a traditional data engineering management role. The ideal candidate has a solid data science and statistical foundation and can connect advances in experimentation methodology with scalable production systems. You will help set technical and scientific direction, translating new statistical methods and machine learning research into capabilities customers can use reliably at scale.
Key responsibilities include: leading and growing the team responsible for data ingestion, experiment computation, and stats engine; defining technical and scientific strategy for advancing experimentation across Statsig Cloud and warehouse-native deployments; partnering with data scientists and engineers to turn new statistical and causal inference methods into scalable, reliable product capabilities; evolving data and computation architecture to support increasingly complex experiment designs, metrics, and customer datasets; and engaging with customers to understand experimentation challenges and translate them into platform improvements.
Required qualifications: strong data science background with hands-on experience in experimentation, statistics, or causal inference (data engineering experience alone is insufficient); experience leading teams of 10-15 members building and productionizing statistically rigorous, data-intensive products; familiarity with experimentation methods such as variance reduction, sequential testing, Bayesian inference, causal effects modeling, or heterogeneous treatment effects; experience building large-scale data ingestion and distributed computation systems across cloud and data warehouse environments; and ability to connect statistical innovation, data architecture, and customer needs to define a compelling experimentation roadmap.
Valued experience includes: advanced degree in statistics, mathematics, computer science, economics, or quantitative field; experience with experimentation platforms, feature management systems, product analytics, or machine learning infrastructure; experience building warehouse-native products or executing computation within Snowflake, BigQuery, Databricks, or similar environments; and experience supporting experimentation for large-scale consumer products, B2B products, marketplaces, social networks, or other settings with complex units of analysis.
