Developer productivity is something every engineering team wants more of, but measuring it well is harder than it looks. The right signals can show you where the real friction is, help you make the case for better tooling, and give your team a clear picture of what's actually improving. Done thoughtfully, measurement becomes one of the most powerful levers you have for getting better at your craft. When you understand how a productivity program works, where the data comes from, and which metrics genuinely drive improvement, you can shape a system that helps you and your team do your best work.
We'll cover the foundations and the traps. You'll learn the established frameworks, DORA and SPACE, and what each is good and bad at. We'll dig into why no single metric tells the truth, and how tension metrics guard against gaming and unintended incentives. We'll build the instinct to interrogate a metric before trusting it, pick up just enough statistics (percentiles, distributions) to read data honestly, and learn to combine quantitative signals with qualitative ones like developer sentiment. Throughout, we'll keep measurement tied to outcomes, connecting it to real business and team goals, and apply the same lens to a timely question: how do you measure the ROI of AI tooling without getting fooled by adoption numbers?
Just as importantly, we'll tackle the human side: how to introduce metrics without eroding trust, why measuring individuals usually backfires, and how a foundation of psychological safety produces a healthier, more motivated culture of improvement. Through hands-on exercises, you'll practice spotting anti-patterns and designing counter-balancing metrics, and you'll leave with a starter set of metrics and tension metrics you can pilot and own with your own team.
When teams are healthy, happy, and productive, everyone wins: the engineer, the team, the org, and the business. You'll leave ready to be a champion for measurement that earns that outcome.
Key Takeaways
1 Know the established frameworks: DORA and SPACE and when to reach for each.
2 Understand how to accurately interpret a metric: what it hides, what behavior it rewards, and how to counter-balance it.
3 Combine quantitative data with qualitative signals like developer sentiment for a more holistic picture.
4 Recognize common anti-patterns such as vanity metrics and measuring individuals, and why a foundation of psychological safety produces a healthier culture.
5 Measure AI ROI by focusing on outcomes rather than adoption or usage numbers in isolation.
Speaker
Erin Doyle
Founding Engineer @Quotient, 20+ Years Across Full Stack Development in Web and Mobile, and Platform Engineering
Erin Doyle is a Founding Engineer at Quotient with over 20 years of experience building across the full stack, from mobile and web products to the platform architectures that power them. Throughout two decades in the trenches, she has developed a deep obsession with the "human infrastructure" of software—specifically how engineering culture and developer experience (DevEx) dictate technical success. A frequent speaker and writer, she is dedicated to helping teams navigate the psychological shifts required by modern engineering, moving past the hype to build high-trust, high-velocity organizations.
