Experienced Engineering Manager, Network Enablement and Access (nea)

Plaid · Fintech · New York, NY · All Cost Centers

Engineering Manager for Plaid's Network Enablement and Access (NEA) organization, focusing on leading a large engineering team (50+ engineers, 6 managers) through an AI transformation. The role involves setting engineering strategy, managing managers, owning technical direction for platforms including ML/data pipelines, and driving hiring. A key aspect is applying AI to engineering workflows and operational processes, with a push towards agentic systems for autonomous issue resolution. Experience leading AI transformations in engineering orgs and shipping data/insights products are nice-to-haves.

What you'd actually do

  1. Set engineering strategy for NEA in partnership with the Product Area Lead. Balance product delivery with foundational investment across eight teams.
  2. Manage 6 engineering managers. Coach, calibrate performance, develop the management bench.
  3. Own technical direction for NEA's platform: integration frameworks, automation infrastructure, ML/data pipelines, and customer-facing surfaces.
  4. Drive recruiting and hiring across the org. Set the bar, own pipeline health, make final hiring decisions for 10+ open roles.
  5. Partner cross-functionally with product, data, operations, and business teams on roadmap priorities and tradeoffs.

Skills

Required

  • 10+ years of software engineering experience
  • 5+ years in engineering management
  • 2+ years leading large engineering organizations (40-50+ people) at the director level or equivalent
  • Track record of building technical foundations that created compounding returns
  • Experience working cross-functionally with product, data, operations, and business teams as a true partner

Nice to have

  • Experience leading AI transformation in an engineering org
  • Experience shipping data/insights products
  • Fintech background

What the JD emphasized

  • AI transformation
  • agentic systems
  • AI changes the way engineering orgs build software
  • ML/data pipelines

Other signals

  • applying AI not just to our products, but to the engineering workflows, operational processes, and technical infrastructure
  • dramatically reduced the cost of building new integrations with AI
  • rethinking how the org itself operates
  • pushing toward agentic systems that can observe, diagnose, and repair issues autonomously
  • strong point of view on how AI changes the way engineering orgs build software