Engineering Manager, Data Platform & ML Ops - Fingerprint

About the Role

Fingerprint is 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, ideally within a startup or high-growth company.
• 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 (technical credibility on both sides of the house required).
• 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 familiarity with technologies: ClickHouse, DataBricks, dbt, Prefect, DataHub, AWS SageMaker, AWS, 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.
• Must be authorized to work from your home location in the United States (visa sponsorship is not available).