Engineering Manager, Fingerprint

• 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

Desirable

• 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