Engineering Manager (Experimentation Data Infrastructure)

As an organization, we deliver for our customers by living our values. We operate from a place of humility, take ownership of problems and successes, approach challenges with a growth mindset, and put our customers at the center of everything we do.

Amplitude’s Commitment to Diversity Equity & Inclusion (DEI): Amplitude believes that diversity enables the creation of better products, improves the ability to solve complex problems, and drives more powerful solutions. We strive to create an environment of inclusion—one focused on psychological safety, empathy, and human connection—that will allow employees of all backgrounds to thrive.

About the Role & Team

We’re looking for an Engineering Manager to lead the Data Infrastructure team within Statsig Experiment at Amplitude. You will lead a multidisciplinary team of software engineers, data engineers, and data scientists responsible for the systems that power experimentation at scale.

The team owns three critical areas:

• 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.

• Data computation: Building distributed computation systems that transform raw data into accurate, timely experiment results.

• Stats engine: Developing and productionizing the statistical methods that help customers make trustworthy decisions from their experiments.

This is not a traditional data engineering management role. We are looking for a leader with a solid data science and statistical foundation who can connect advances in experimentation methodology with scalable production systems. You will help set our technical and scientific direction, translating new statistical methods and machine learning research into capabilities that customers can use reliably at scale.

You’ll partner closely with data scientists, engineers, product managers, and customers to advance the state of experimentation. The ideal candidate is equally comfortable discussing causal inference and statistical power with data scientists, distributed computation architectures with engineers, and experimentation strategy with customers.

What You’ll Do

• Lead and grow the team responsible for Statsig’s data ingestion, experiment computation, and stats engine.

• Define the technical and scientific strategy for advancing experimentation across both Statsig Cloud and warehouse-native deployments.

• Partner with data scientists and engineers to turn new statistical and causal inference methods into scalable, reliable product capabilities.

• Evolve our data and computation architecture to support increasingly complex experiment designs, metrics, and customer datasets.

• Engage with customers to understand their experimentation challenges and translate them into platform and methodology improvements.

You’ll Be a Great Addition to the Team If You Have

• A strong data science background, with hands-on experience in experimentation, statistics, or causal inference. Experience solely in data engineering is not sufficient for this role.

• Experience leading teams (10-15 team members) that build and productionize 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.

• The ability to connect statistical innovation, data architecture, and customer needs to define a compelling experimentation roadmap.

Particularly Valuable Experience

• An advanced degree in statistics, mathematics, computer science, economics, or another 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.

• Experience supporting experimentation for large-scale consumer products, B2B products, marketplaces, social networks, or other settings with complex units of analysis.

Why This Role Matters

Experimentation platforms cannot advance through infrastructure work or statistical research alone. New methodologies only create value when they can be implemented correctly, computed efficiently, and delivered through systems customers can trust.

This leader will bring those disciplines together. You will help build the data and computation foundation for Statsig while creating an environment where new data science innovations can move from research and prototypes into production—advancing both our product and the broader field of experimentation.