Engineering Manager

Fingerprint empowers developers to stop online fraud at the source. They are hiring Engineering Managers across four of their product and platform teams, where each role involves leading teams responsible for key aspects of fraud detection and device intelligence.

Responsibilities:

• Lead the team. Own 1:1s, performance calibrations, career development, and hiring. Set the bar for code quality, on-call discipline, and delivery. Coach intentionally — especially engineers approaching Staff
• Drive execution. Own sprint planning and roadmap delivery. Manage scope, urgency, and quality tradeoffs — know when to ship and when to slow down. Run tight processes: planning, incident response, on-call rotations, postmortems
• Stay technically grounded. Maintain enough depth in your team's stack to review designs, challenge architectural decisions, and surface risks before they become incidents. Earn IC trust through substance, not just title
• Coordinate across teams. Represent your team in technical discussions across Engineering. Manage cross-team dependencies proactively and communicate tradeoffs clearly to PMs and leadership
• Shape your team's strategy. Partner with Engineering Leadership and Product on roadmap prioritization. Translate technical constraints and opportunities into executable plans that move the business forward

Requirements:

• 2–5+ years of engineering management experience, with direct ownership of performance management, career development, and hiring — not just running standups
• Experience managing teams of 4–10 engineers in a fast-paced, iterative environment
• Technical depth in your team's domain: you can review designs, challenge architectural decisions, and earn credibility with senior engineers through substance
• A delivery track record — concrete examples of quarters where your team shipped reliably, and what you specifically did when they didn't
• Strong cross-functional communication: you translate technical constraints into language PMs, leadership, and other stakeholders can act on
• Experience in a high-growth B2B SaaS or API-first company
• Familiarity with fraud detection, identity, device intelligence, or trust & safety domains
• ML-in-production exposure: you've worked alongside ML engineers or data scientists shipping models and understand the operationalization challenges at a high level
• Experience with data infrastructure, ML systems, or both — particularly relevant for Data Platform & ML Ops and Identification Accuracy
• Familiarity with latency-sensitive or real-time systems where p95/p99 latency is a product quality metric