Engineering Manager, Data & Experiments Platform

Amplitude; San Francisco (CA); On-site; USD 254,000 - 381,000; Full time

Benefits offered by this job

Excellent medical, dental and vision

401(k) with employer match

Flexible time off

Employee stock purchase program

Job summary

Amplitude is seeking an Engineering Manager to lead the Data Infrastructure team within Statsig Experiment, guiding engineers, data engineers, and data scientists responsible for scalable experimentation systems. You will shape technical and scientific direction and partner across teams to deliver reliable capabilities.

The role emphasizes combining statistical innovation with production-ready data architectures to meet customer needs in cloud and warehouse-native deployments.

Qualifications

• Strong data science background with experimentation, statistics, or causal inference.
• Experience leading teams (10–15 members) delivering data-intensive products.
• Ability to connect analytics research with scalable production systems.

Responsibilities

• Lead and grow the data ingestion, experiment computation, and stats engine teams.
• Define technical and scientific strategy for experimentation across cloud deployments.
• Turn statistical methods into scalable product capabilities with engineers and data scientists.
• Evolve data architectures to support complex experiment designs and datasets.
• Engage with customers to translate experimentation challenges into platform improvements.

Skills

Data science

Experimentation

Statistics

Causal inference

Leadership (10-15)

Cloud data platforms

Education

Advanced degree in quantitative field

Tools

Snowflake

BigQuery

Databricks

Job description

Amplitude is seeking an Engineering Manager to lead the Data Infrastructure team within Statsig Experiment, guiding engineers, data engineers, and data scientists responsible for scalable experimentation systems. You will shape technical and scientific direction and partner across teams to deliver reliable capabilities.

The role emphasizes combining statistical innovation with production-ready data architectures to meet customer needs in cloud and warehouse-native deployments.