Engineering Manager (Data Engineering)

<div class="content-intro"><p>Amplitude is the leading AI analytics platform, helping over 4,700 customers—including Atlassian, Burger King, NBCUniversal, and Square—build better products and digital experiences. With powerful AI Agents embedded across our platform, teams can analyze, test, and optimize user experiences faster than ever. Ranked #1 across multiple categories in G2’s Winter 2026 Report, Amplitude is the best-in-class solution for product, data, and marketing teams. Learn more at <a href="http://amplitude.com">amplitude.com</a>.</p> <p>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.</p> <p><strong>Amplitude’s Commitment to Diversity Equity &amp; Inclusion (DEI): </strong>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.</p></div><h3>About the Role &amp; Team</h3> <p>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.</p> <p>The team owns three critical areas:</p> <ul> <li><strong>Data ingestion: </strong>Collecting and importing experiment exposures, custom events, OpenTelemetry data, and real user monitoring data across SDKs, streaming systems, cloud storage, and customer data warehouses.</li> <li><strong>Data computation: </strong>Building distributed computation systems that transform raw data into accurate, timely experiment results.</li> <li><strong>Stats engine: </strong>Developing and productionizing the statistical methods that help customers make trustworthy decisions from their experiments.</li> </ul> <p>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.</p> <p>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.</p> <h3>What You’ll Do</h3> <ul> <li>Lead and grow the team responsible for Statsig’s data ingestion, experiment computation, and stats engine.</li> <li>Define the technical and scientific strategy for advancing experimentation across both Statsig Cloud and warehouse-native deployments.</li> <li>Partner with data scientists and engineers to turn new statistical and causal inference methods into scalable, reliable product capabilities.</li> <li>Evolve our data and computation architecture to support increasingly complex experiment designs, metrics, and customer datasets.</li> <li>Engage with customers to understand their experimentation challenges and translate them into platform and methodology improvements.</li> </ul> <h3>You’ll Be a Great Addition to the Team If You Have</h3> <ul> <li>A strong data science background, with <span style="text-decoration: underline;"><em><strong>hands-on </strong></em></span>experience in experimentation, statistics, or causal inference. Experience solely in data engineering is not sufficient for this role.</li> <li>Experience leading teams (10-15 team members) that build and productionize statistically rigorous, data-intensive products.</li> <li>Familiarity with experimentation methods such as variance reduction, sequential testing, Bayesian inference, causal effects modeling, or heterogeneous treatment effects.</li> <li>Experience building large-scale data ingestion and distributed computation systems across cloud and data warehouse environments.</li> <li>The ability to connect statistical innovation, data architecture, and customer needs to define a compelling exp