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 Git Hub).

We have raised $77M and are backed by Craft Ventures (previously invested in Tesla, Facebook, Airbnb ), Nexus Venture Partners (previously invested in Postman, Apollo.io, MinIO, Druva) and Uncorrelated Ventures (previously invested in Redis, Rollbar & Gradle).

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.

We believe that diverse perspectives fuel innovation, and we encourage candidates from all backgrounds and experiences to apply.

• 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.

• 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.

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

• Data Platform:
  Click House, Data Bricks, dbt, Prefect, Data Hub
• ML Ops: AWS Sage Maker
• Infrastructure: AWS

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. To comply with local legislation and provide greater transparency, we share salary ranges on all job postings. However, these ranges are specific to the hiring location and may differ within or outside the US.

Offers vary depending on, but not limited to, relevant experience, education, certifications/licenses, skills, training, and market conditions.

Due to regulatory and…