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Amplitude is seeking an Engineering Manager to lead the Data Infrastructure team within Statsig Experiment, the company's experimentation platform. You will manage a multidisciplinary team of 10-15 software engineers, data engineers, and data scientists responsible for building systems that power 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 role uniquely blends data science, statistics, and systems engineering. You will define technical and scientific strategy, translating advances in experimentation methodology and causal inference research into scalable, production-ready capabilities. You'll partner closely with data scientists, engineers, product managers, and customers to advance the state of experimentation.
Key responsibilities include leading and growing the team, defining technical and scientific strategy for Statsig Cloud and warehouse-native deployments, partnering with data scientists to turn statistical and causal inference methods into product capabilities, evolving data and computation architecture to support complex experiment designs and large customer datasets, and engaging with customers to understand experimentation challenges.
Ideal candidates have a strong data science background with hands-on experience in experimentation, statistics, or causal inference (data engineering experience alone is insufficient). You should have experience leading teams building statistically rigorous, data-intensive products, familiarity with advanced experimentation methods (variance reduction, sequential testing, Bayesian inference, causal effects modeling, heterogeneous treatment effects), and experience building large-scale data ingestion and distributed computation systems across cloud and data warehouse environments. An advanced degree in statistics, mathematics, computer science, economics, or a quantitative field is particularly valuable, as is experience with experimentation platforms, feature management, product analytics, or ML infrastructure.
