Engineering Enablement by DX

Sejal Amin is the Chief Technology Officer at Priceline, where she leads product engineering, infrastructure, data, and technology operations. Pedro Gutierrez is Senior Director of Software Engineering, where he has helped drive developer experience initiatives and the adoption of Priceline's product operating model.
In this episode of the Engineering Enablement podcast, Justin Reock talks with Sejal and Pedro about Priceline's journey from a project-based organization to a product operating model and the role developer experience played in making that transformation successful. They discuss how DX metrics and developer feedback helped identify organizational bottlenecks, guide structural changes, empower engineering managers, and build trust across teams. They also explore the importance of clear communication, creating a dedicated developer experience team, and how their operating model has helped prepare Priceline for AI-driven software development.

Where to find Sejal Amin:  
• LinkedIn: https://www.linkedin.com/in/sejal-amin
Where to find Pedro Gutierrez: 
• LinkedIn: https://www.linkedin.com/in/pedro-gutierrez-b6605422
Where to find Justin Reock:
• LinkedIn: https://www.linkedin.com/in/justinreock

In this episode, we cover:
(00:00) Intro
(01:07) Meet Sejal Amin and Pedro Gutierrez
(01:47) How Priceline's developer experience journey began
(04:55) Lessons from Priceline's first developer experience surveys
(06:55) How DX improved Priceline's developer experience surveys
(09:47) Identifying the causes of organizational slowness
(12:33) How the product operating model changed the way Priceline works
(14:10) Priceline's phased rollout with DX
(18:14) How DX insights drove organizational changes
(19:33) Why Priceline improved developer experience before org change was complete
(22:18) How clear communication builds trust
(24:25) Early results from Priceline's Core Four
(25:38) Creating a culture of continuous feedback to build trust
(27:40) What has changed in the engineering manager role
(30:10) Resources for learning about the product operating model
(32:40) What Pedro learned from implementing DX
(34:51) The developer experience team
(35:59) How AI tools have impacted Priceline’s teams 
(37:20) How the product operating model supports AI-driven development
(39:13) Final advice for engineering leaders

Referenced:
• Measuring developer productivity with the DX Core 4
• Transformed: Moving to the Product Operating Model (Silicon Valley Product Group) 
• Team Topologies
• Flow Framework
• Project to Product: How to Survive and Thrive in the Age of Digital Disruption with the Flow Framework

Indeed increased AI coding tool adoption from roughly 25% to 97% across its engineering organization, but getting engineers to use the tools was only part of the challenge.
In this session from DX Annual, Michael Redding, Principal Product Manager, and Jeff Davis, VP of Core Infrastructure at Indeed, explain how the company used structured training, leadership support, and ongoing community engagement to help more than 2,000 engineers build practical AI skills. They share why an early train-the-trainer model fell short, how they redesigned their approach around hands-on learning, and what they learned about balancing adoption, measurement, and psychological safety.
They also discuss the impact of the program on coding time, the role of continuous enablement after formal training ended, and how Indeed is preparing for the next phase of AI adoption, including agentic workflows and AI-powered coaching.

Where to find Jeff Davis: 
• LinkedIn: https://www.linkedin.com/in/utjeffd 
Where to find Michael Redding:
• LinkedIn: https://www.linkedin.com/in/reddingsetgo

In this episode, we cover:
(00:00) Intro
(01:05) Indeed's DX survey from January 2025
(02:30) The two-part strategy to double engineering productivity
(04:21) How Indeed increased AI adoption from 25% to 97%
(15:40) Results from Indeed's AI training program
(18:33) How Indeed sustains AI adoption and learning
(23:06) What's next for AI enablement at Indeed
(24:41) Q&A: How coding time was calculated
(25:25) Q&A: How Indeed uses AI playbooks
(26:40) Q&A: Balancing asynchronous and live AI training
(28:22) Q&A: Psychological safety during AI adoption
(31:44) Q&A: Why AI adoption spikes after the holidays
(33:20) Q&A: The metrics Indeed tracked 
(35:22) Q&A: Where the time savings are going 
(36:54) Q&A: Reaching engineers who skipped the training
(38:08) Closing thoughts

Referenced:
• Indeed
• Claude Code | Anthropic's agentic coding system
• Cursor
• Windsurf
• Amp Code
• The Complete Guide to Building Skills for Claude | Anthropic
• Measuring developer productivity with the DX Core 4

In this closing panel from DX Annual, Rafe Colburn, Chief Product and Technology Officer at Etsy; Jesse Adametz, Senior Director of Engineering, Platform Engineering at Twilio; Eirini Kalliamvakou, Research Advisor at GitHub; Collin Green, Senior Staff UX Researcher at Google; and Brian Houck, Senior Principal Applied Scientist at Microsoft debate some of the biggest questions surrounding AI and engineering productivity.
They discuss whether AI will reduce the need for engineers, how AI is affecting technical debt, the future role of software engineers in an agentic world, and whether organizations should mandate AI adoption. They also explore how bottlenecks are shifting across the software development lifecycle, the challenges facing junior engineers, and why learning, culture, and change management may ultimately matter more than the tools themselves.

Where to find Rafe Colburn:
• LinkedIn: https://www.linkedin.com/in/rafeco
• Blog: https://rafe.codes
Where to find Eirini Kalliamvakou: 
• LinkedIn: https://www.linkedin.com/in/eirini-kalliamvakou-1016865
• X: https://x.com/irina_kAl
Where to find Brian Houck: 
• LinkedIn: https://www.linkedin.com/in/brianhouck
Where to find Jesse Adametz: 
• LinkedIn: https://www.linkedin.com/in/jesseadametz 
• X: https://x.com/jesseadametz 
• Website: https://www.jesseadametz.com 
Where to find Collin Green: 
• LinkedIn: https://www.linkedin.com/in/collin-green-97720378

In this episode, we cover:
(00:00) Intro
(01:16) Why an AI-first SDLC doesn’t mean fewer engineers 
(03:09) The debate over AI and technical debt
(07:40) AI-generated code and the future role of engineers
(14:16) Why mandating AI use doesn't necessarily lead to better outcomes
(20:43) Predictions for the future of junior engineers 
(23:22) Where the bottlenecks are in the SDLC now
(28:25) How risk influences AI use 
(32:38) Why the human side is the biggest AI adoption challenge

Referenced:
• Etsy
• GitHub
• Microsoft
• Twilio
• Google 
• Stewart Reichling
• What is the SPACE framework and when should you use it?

In this session from DX Annual, Uma Namasivayam, Senior Director of Engineering Productivity at Dropbox, shares how the company's developer productivity efforts evolved from improving developer experience to preparing for the agentic era.
He explains how Dropbox approached AI adoption across its engineering organization, the impact it had on developer productivity, and why faster code generation is creating new bottlenecks in areas such as code review, validation, and CI/CD. He also discusses Dropbox's efforts to rethink engineering systems, measurement, and workflows, including the development of agentic tooling and new metrics designed to move beyond PR throughput and toward product velocity.

Where to find Uma Namasivayam:
• LinkedIn: https://www.linkedin.com/in/unamasivay

In this episode, we cover:
(00:00) Intro
(00:57) The beginning of Dropbox’s DX journey
(02:34) AI adoption at Dropbox: what made it work  
(04:46) The results of Dropbox's AI adoption efforts
(05:39) What the results mean for the business 
(06:55) The phases of AI adoption and where they are now
(08:00) The new bottlenecks
(09:16) Three challenges Dropbox faces moving into agentic engineering
(10:05) How Dropbox is redesigning the SDLC for agentic engineering
(15:46) The new metrics that matter 
(19:16) Final takeaways

Referenced:
• Dropbox 
• Developer Experience Index (DXI) | DX 
• DX Core 4 Productivity Framework
• Cursor
• Claude Code | Anthropic's agentic coding system
• JetBrains 
• Visual Studio Code
• Jira | Project Management for the AI Era | Atlassian
• GitHub

In this session from DX Annual, Christopher Sanson, Product Lead, AI Developer Experience, and Madison Capps, Engineering Manager, Infrastructure at Airbnb, challenge some of the most common assumptions about AI. Is AI primarily about replacing humans? Do organizations need mandates to drive adoption? And are the productivity gains really as small as some studies suggest?
Using examples from Airbnb's own AI journey, they share how the company achieved widespread adoption of agentic AI through AirChat, community enablement, and internal tooling rather than top-down mandates. They also discuss the impact AI is having on developer productivity, how non-developers are increasingly using coding tools, and how teams are rethinking product development in an AI-first world.
Finally, Madison takes a deeper look at the infrastructure powering Airbnb’s AI strategy, including AirChat CLI, the AirChat SDK, and AirChat Remote, along with the company’s vision for asynchronous agent workflows and the next generation of AI-powered development.

Where to find Christopher Sanson:
• LinkedIn: https://www.linkedin.com/in/christophersanson 
Where to find Madison Capps:
• LinkedIn: https://www.linkedin.com/in/madison-capps-66950625

In this episode, we cover:
(00:00) Intro
(01:37) Myth #1: AI is about replacing humans
(03:22) Myth #2: You need mandates to drive AI adoption
(05:21) AirChat, agentic AI, and Airbnb's adoption strategy
(08:07) Myth #3: AI has little impact on productivity
(09:33) Airbnb's increase in coding time and PR throughput
(14:20) Myth #4: AI coding tools are just for coders
(15:39) How non-developers are using coding tools
(17:24) Rethinking product development in an AI-first world
(20:30) Myth #5: Vibe coding isn’t coding
(22:16) Unsolved problems in agentic AI tooling and how Airbnb is addressing them
(26:30) Airbnb’s overall AI philosophy in practice
(29:15) Using agentic AI to accelerate code migrations
(30:18) AirChat SDK: How Airbnb enables teams to build AI-powered applications
(33:17) AirChat Remote and asynchronous agent workflows
(36:07) Predictions for what’s next

Referenced:
• ⁠Airbnb
• Steve Jobs’s Bicycles for the Mind 
• Jennifer St Pierre 
• Justin Reock
• AI-generated merged code holds steady at ~30%
• Andrej Karpathy's post on X