Engineering Manager, Data Platform & ML Ops

Fingerprint empowers developers to stop online fraud at the source. We work on turning radical new ideas in the fraud detection space into reality. Our products are developer-focused and our clients range from solo developers to publicly traded companies. We are a globally dispersed, 100% remote company with a strong open-source focus. Our flagship open-source project is FingerprintJS (27K stars on GitHub). We have raised $77M and are backed by Craft Ventures, Nexus Venture Partners, and Uncorrelated Ventures.

About the role

We are looking for an Engineering Manager to join our Data Platform & ML Ops team. In this role, you will lead the team responsible for Fingerprint's data foundation — from our internal data warehouse that powers business intelligence and product analytics, to the full ML Ops lifecycle that turns raw signals into production models. You'll foster a culture of high performance, helping engineers grow while delivering the reliable, scalable infrastructure our identification and smart signals products depend on.

Responsibilities

• Lead and mentor a team of 4-6 engineers spanning data platform and ML operations.
• Own the reliability, scalability, and evolution of Fingerprint's internal data warehouse — the foundation for business analytics and a direct input to our flagship identification and smart signals products.
• Oversee the full ML Ops lifecycle end-to-end: experimentation, training pipelines, model deployment, and production monitoring.
• Provide technical leadership by collaborating with senior engineers, guiding architecture decisions, and reviewing complex technical proposals.
• Work closely with data scientists, product managers, data analysts and engineering leads to translate data and ML investments into measurable product outcomes.
• Coach and support engineer growth, promoting continuous learning across a fast-moving data and ML landscape.
• Define and evolve platform standards, tooling, and best practices across both domains.

Requirements

• Minimum of 2 years of experience leading data engineering, ML engineering, or platform teams in an agile environment.
• At least 5 years of professional experience in data engineering, ML engineering, or adjacent software engineering, particularly within SaaS.
• Hands-on experience in both data infrastructure and ML systems is a must — you don't need to be an expert in both, but you should be technically credible on both sides of the house.
• Strong technical background across data infrastructure and ML systems.
• Experience managing engineers across multiple technical disciplines.
• Proven ability to lead teams shipping high-reliability data products that prioritize quality and user impact.
• Demonstrated success driving change and innovation in fast-paced, scaling environments.

Preferred Qualifications

• Experience leading teams in a startup or high-growth environment.
• Familiarity with analytical storage systems such as ClickHouse, DataBricks, Snowflake, or BigQuery.
• Experience with ML lifecycle tooling — training pipelines, model serving, and production monitoring.
• Experience with AWS and cloud-based data and ML infrastructure.

Technologies

• Data Platform: ClickHouse, DataBricks, dbt, Prefect, DataHub
• ML Ops: AWS SageMaker
• Infrastructure: AWS

Pay

For US-based employees, the cash compensation range for this role is $159,000 – $215,000. We set standard ranges for all US roles based on function, level, and geographic location, benchmarked against similar stage growth companies.