Engineering Manager, Network Health & Risk

Plaid; San Francisco (CA); On-site; USD 180,000 - 240,000; Full time

Benefits offered by this job

Equity

401(k)

Comprehensive benefits

Job summary

Plaid is seeking an Engineering Manager for Network Health to lead a growing, highly technical team focused on risk engineering across Plaid products. You will shape the long-term strategy, translate fraud scenarios into priorities, and drive execution with a high technical bar.

You will partner with embedded data scientists and ML engineers, stay hands-on with data to identify trends, and guide architecture and system design in a dynamic environment.

Qualifications

• 8+ years of industry experience, including time as a Staff-level engineer before transitioning into management.
• 2+ years of engineering management experience
• Track record of setting strategy and executing roadmaps in ambiguous environments while balancing business, product, and technical tradeoffs.
• Deep technical judgment in backend and data-intensive systems; architecture, system design, coding, and data analysis.
• Experience in risk engineering, fraud detection, abuse prevention, trust and safety, or related domains.
• Track record of building and growing high performing engineering teams.

Responsibilities

• Lead a team of engineers and embedded data scientists, with clear goals, coaching, and feedback.
• Drive the long-term Network Health strategy and roadmap with Plaid's technical and product leaders.
• Uphold high technical quality and delivery velocity, leading by example.
• Dive hands-on into data and tech stack to guide decisions based on metrics and trends.

Skills

Team leadership

Strategy & roadmaps

Technical architecture

Data analysis

Backend systems

Risk engineering

Job description

Plaid is seeking an Engineering Manager for Network Health to lead a growing, highly technical team focused on risk engineering across Plaid products. You will shape the long-term strategy, translate fraud scenarios into priorities, and drive execution with a high technical bar.

You will partner with embedded data scientists and ML engineers, stay hands-on with data to identify trends, and guide architecture and system design in a dynamic environment.