Software Engineering Daily – “Developer Experience at Capital One with Catherine McGarvey”

Date: January 13, 2026
Host: Shawn Falconer
Guest: Catherine McGarvey, SVP of Developer Experience at Capital One

Episode Overview

This episode explores the evolving landscape of developer experience (DevEx) at enterprise scale, with an emphasis on how a large, regulated financial institution like Capital One enables its 14,000 technologists. Catherine McGarvey shares lessons on balancing agility, security, and scale, the practicalities of measuring developer productivity, effective adoption of generative AI, and the future of engineering roles. The conversation is candid, thoughtful, and rooted in both strategic insight and hands-on experience.

Key Themes & Discussion Points

1. The Evolution of Developer Enablement

• Diverse Experience & Perspective
  Catherine attributes her approach to DevEx to her varied background—having worked in startups, defense consulting, and now leading teams at Capital One.

  • Quote (02:03):
    "You learn a couple of things that often there's not one way to do something ... you’ve really got to think about what matters in how you're doing software development to the consumer and to the business..."

• Defining Developer Enablement at Scale
  For Catherine, enablement blends speed and throughput with close attention to user needs—balancing tools for developers with robust feedback loops.

  • Quote (03:41):
    "When I think about developer enablement, it's combining speed and throughput ... but it has to be balanced by, are we listening to users? ... Those two things in balance ... is where enablement is really about."

2. Measuring Developer Productivity

• Nuanced Metrics for Large Organizations

  • No silver bullet, but focus matters: metrics include time to deploy, onboarding (time to first/10th commit), and tool impact (NPS, outcomes).
  • Recognizes limitations and the “gaming” of metrics.
  • Quote (06:13):
    "I don't think there's one great answer, but there's a lot of good things you can measure..."

• Onboarding & Retention

  • Effective onboarding (time to first commit) boosts satisfaction and engineering confidence.
  • Memorable moment (09:07):
    "If it takes you six months to get to a point where you've done anything ... that's probably going to lead to a certain ... dissatisfaction..."

3. Agility in a Regulated Environment

• Standardizing “the Right Things”

  • Capital One's approach: “Make it easy to do the right thing.” Defaults are provided for databases, platforms, tools—deviations require explicit process.
  • Standardization enables migration, security, and best practices.
  • Quote (10:23):
    "Instead of having to pick through what database to use, this is the one we recommend ... making it easy to just pick the thing, it doesn't become a question."

• Flexibility for Innovation

  • Teams can experiment—often new standards are informed bottom-up when proliferation reveals opportunity.

4. AI in Enterprise Development

• Experimentation & Abstraction Over Lock-in

  • AI tooling—especially agents and LLMs—is approached through abstraction layers and open standards, preserving adaptability as the tech rapidly changes.
  • Quote (14:44):
    "How locked in will you be if it's easy to migrate now because you have tools that enable that migration?"

• Coding Assistants: Adoption & Impact

  • Overcoming early fears about AI (job loss) via communication from leadership, pilots, and highlighting peer success stories.
  • Training moves from prompt engineering to use-case-centric, opinionated tool recommendations.
  • Quote (15:43):
    "...we want to automate anything that isn't part of the creative problem solving... Let's try it and see what works..."

• Measuring AI ROI

  • Careful not to measure lines of code; focuses on qualitative improvements and whether AI provides real lift (e.g., code review, migration, dependency resolution).
  • Quote (18:23):
    "Perhaps the worst coding assistants or agents might produce the largest amount of code and that's actually not a measure of value in any way."

• Differential Impact Based on Experience

  • Junior devs gain most from AI for onboarding, exploring codebases; senior devs find value as planning/tools mature.
  • Human-in-the-loop remains vital.
  • Quote (25:50):
    "...the better the response, the more senior you are, the more you might be expecting from that response..."

5. The Developer Role: Present and Future

• Fundamental Shifts

  • Engineers’ roles are evolving: less rote work, more focus on judgment, direction, and creative problem solving.
  • Quote (26:49):
    "You can get up to speed on a code base pretty quickly ... you can improve the quality or the frequency of what you're able to produce ... by using a coding assistant ... It is, I think, changing the role of the engineer..."

• Resistance & Encouragement

  • Some engineers may resist loss of “low-level” work; Catherine urges openness to new opportunities and leveraging “lift.”
  • Quote (28:56):
    "If you're an engineer that values resolving NPM dependencies ... this evolution is not for you. Right. This is going to take you out of that space..."

6. Beyond Coding: The Other 80%

• Investments in Efficiency
  • Focused on automating and streamlining non-coding tasks: triage, deployments, pipelines, documentation, coordination.
  • AI also leveraged for documentation and improving non-coding workflows.
  • Quote (30:58):
    "If I think about things that aren't code ... it can be triaging issues in production ... getting code to production ... understanding customers..."

7. What Peak Developer Productivity Looks Like

• Frictionless Idea to User

  • Dream state: a developer has an idea and gets it to a user securely and rapidly, with minimal handoffs.
  • Quote (32:58):
    "I have an idea and I can get it in front of a user that day ... That’s the thing I’m super excited about..."

• Choosing the Right Metrics

  • Avoid vanity or easily gamed metrics (e.g., # of PRs). Instead, measure cycle time from “story start to production,” quality progression, and team flow.
  • Memorable exchange (34:27):
    B: "Yeah, just make smaller PRs." A: "Yeah, make smaller PRs. ... I can generate a whole bunch of PRs for you..."

Notable Quotes & Moments (with Timestamps)

• On Measurement:
  "Developers … are fantastic at gaming metrics." — Catherine [08:57]

• AI & Role Change:
  "You've got another person almost sitting next to you... the next paradigm shift is you’ve got another person doing the work for you." — Catherine [26:49]

• Advice to Engineering Leaders:
  "One shouldn't assume large means slow... If you can identify the routine that exists already and jump onto it and sort of add to it or iterate on it, then you can ... get that speed of change happening..." — Catherine [39:03]

Key Timestamps

• 01:20 – Catherine’s career background & approach to developer enablement
• 03:30 – Defining developer enablement at Capital One
• 06:00 – Measuring DevEx success; deploying at enterprise scale
• 08:00 – Onboarding: metrics and impact
• 10:15 – Agility, regulatory compliance, and standardization
• 12:40 – When/how to standardize vs. allow tool choice (AI as an example)
• 14:44 – Abstraction and future-proofing for rapid AI tool change
• 15:43 – Rolling out LLM-powered coding assistants: change management
• 18:23 – AI ROI: what to measure and what not to measure
• 25:50 – AI effectiveness: junior vs. senior developer use
• 26:49 – Shifting developer role and future predictions
• 30:58 – Automation and AI in non-coding (“the other 80%”)
• 32:58 – Vision of peak developer productivity
• 34:27 – Pitfalls of tracking bad metrics; story cycle time
• 39:03 – Advice for leaders moving to large organizations
• 40:30 – Final thoughts: developer joy and opportunity

Takeaways

• Balance agility and compliance with selective standardization and enablement.
• Metrics matter—choose wisely, as teams optimize what is measured.
• AI can transform developer experience, but human oversight, creative problem solving, and judgment remain central.
• The role of developers is shifting towards higher levels of abstraction and creativity.
• Meet developers where they are: training, peer-led learning, and tailored tool guidance are essential for successful AI adoption.
• Peak productivity is rapid, secure, value-driven ideation-to-deployment with minimal friction.

Catherine’s message to developers:
“This is a really exciting time to be a developer.” [40:30]