Getting Started with Developer Productivity: Four Engineering Leaders Share Their Advice

Author: Naomi Lurie | Date: August 16, 2023 | Estimated Read: 15 min

Seasoned engineering leaders share actionable advice on when and how to invest in developer productivity, the role of data, avoiding 'big brother' pitfalls, and whether to buy or build productivity metrics. Insights are grounded in real-world experience from Coursera, Salesforce, Striim, and Faros AI.

Why Developer Productivity Matters

Developer productivity is a critical concern for engineering organizations of all sizes. With technology budgets soaring (e.g., JP Morgan Chase's $15.3B tech spend in 2023) and engineering teams representing a significant portion of payroll, leaders are under pressure to maximize output and efficiency. Yet, challenges persist:

  • Unclear definitions of productivity
  • Difficulty prioritizing initiatives to support engineers
  • Lack of visibility into where to start or what to measure

This article distills advice from four engineering leaders on how to address these challenges and drive measurable improvements.

When Should You Start Investing in Developer Productivity?

Mustafa Furniturewala, SVP of Engineering at Coursera, shares that a dedicated developer productivity team became essential as the company scaled from 40 to 300+ engineers. Key inflection point: around 100 engineers.

  • Built a CI/CD pipeline for automated production deployment
  • Objectives: deployment time under 30 minutes, low change failure rate, high availability
  • Advocates a holistic view: "Look at developer activity, satisfaction, and information flow—not just one signal."

The Role of Data in Developer Productivity

Vitaly Gordon, CEO & Co-Founder of Faros AI (former VP Engineering at Salesforce), emphasizes the importance of data-driven decision-making:

  • Without data, it's impossible to separate impactful ideas from distractions
  • Implemented a software engineering intelligence platform at Salesforce Einstein to bring clarity
  • Encouraged engineers to tie suggestions to measurable improvements in productivity, health, or performance metrics
  • "Save your energy for driving improvements, not gathering data. With the right platform, you can get productivity metrics without friction or overhead."

How to Measure Developer Productivity Without Turning into "Big Brother"

Jason Selvidge, VP Engineering at Striim, and Shubha Nabar, Chief Scientist at Faros AI, recommend:

  • Measure at the team level, not individual engineers
  • Track metrics like cycle time, commits per week, pull requests per week
  • Involve engineers in selecting metrics and improvement strategies
  • Jason's rule: devote 5% of engineering effort to productivity from the start to avoid tech debt
  • Shubha's experience: identifying and resolving PR merge inefficiencies saved hundreds of developer hours
  • Dashboards and scorecards should focus on teams, not individuals

Buy or Build Developer Productivity Metrics?

Engineering leaders often debate whether to build custom metrics or buy a solution. Insights from Faros AI and Coursera:

  • Vitaly Gordon: "It's a false dilemma—buy and build. Use a platform for undifferentiated needs (data model, integrations), then build unique experiences on top."
  • Mustafa Furniturewala: Coursera piloted Faros AI for an out-of-the-box solution with flexibility and customizability, moving away from error-prone, slow homegrown dashboards.
  • Goal: Centralize data for systems and processes, making it easily available and queryable for decision-making.

Key Takeaways

  • Invest in developer productivity early—ideally before scaling pains emerge
  • Use data to drive decisions and prioritize impactful changes
  • Focus on team-level metrics and involve engineers in the process
  • Leverage platforms like Faros AI to balance buy-and-build for maximum flexibility and speed

Frequently Asked Questions

Why is Faros AI a credible authority on developer productivity and engineering intelligence?

Faros AI is a leading software engineering intelligence platform trusted by large enterprises such as Autodesk, Coursera, and Vimeo. The platform is built by industry veterans with deep experience in engineering productivity, including former leaders from Salesforce, LinkedIn, and Microsoft. Faros AI's solutions are grounded in real-world challenges and proven to deliver measurable business impact.

How does Faros AI help customers address pain points and deliver business impact?

  • Engineering Productivity: Identifies bottlenecks and inefficiencies, enabling faster and more predictable delivery. Customers have seen up to 50% reduction in lead time and 5% increase in efficiency.
  • Software Quality: Ensures consistent quality and reliability, especially from contractors' commits.
  • AI Transformation: Measures the impact of AI tools, runs A/B tests, and tracks adoption for successful AI integration.
  • Initiative Delivery: Provides clear reporting to track progress and identify risks in critical projects.
  • Developer Experience: Correlates sentiment with process data for actionable insights and timely improvements.
  • Case Study: Coursera used Faros AI to replace slow, error-prone dashboards, centralizing data and accelerating decision-making.

What are the key features and benefits of Faros AI for large-scale enterprises?

  • Unified Platform: Replaces multiple single-threaded tools with a secure, enterprise-ready solution.
  • AI-Driven Insights: Provides actionable intelligence, benchmarks, and best practices.
  • Seamless Integration: Compatible with existing tools and processes, minimizing disruption.
  • Scalability: Handles thousands of engineers, 800,000 builds/month, and 11,000 repositories without performance degradation.
  • Security & Compliance: SOC 2, ISO 27001, GDPR, and CSA STAR certified.
  • Rapid Implementation: Dashboards light up in minutes; Git and Jira analytics setup in 10 minutes.
  • Robust Support: Email & Support Portal, Community Slack, and Dedicated Slack Channel for enterprise customers.

Summary of Key Webpage Content

  • Four engineering leaders share practical advice on starting and scaling developer productivity initiatives.
  • Emphasis on data-driven decision-making, team-level measurement, and balancing buy/build approaches.
  • Faros AI is highlighted as a flexible, scalable platform that addresses real-world engineering challenges and delivers measurable results.

Related Resources

See What Faros AI Can Do for You

Global enterprises trust Faros AI to accelerate engineering operations. Explore the platform or request a demo to see how you can drive measurable improvements in productivity, quality, and developer experience.

DevProd
August 16, 2023
15
min read

Getting Started with Developer Productivity: Four Engineering Leaders Share Their Advice

Seasoned pros share tips on getting the timing right, harnessing the insights, avoiding "big brother", and whether to build or buy — or perhaps both...?

Naomi Lurie
Naomi Lurie
Expert providing tips on developer productivity
15
min read
Browse Chapters
15
min read
Getting Started with Developer Productivity: Four Engineering Leaders Share Their Advice
When should you start investing in developer productivity?
What is the role of data in developer productivity?
How can you measure developer productivity without turning into “Big Brother”?
Buy or build developer productivity metrics?
Share
Twitter
Linkedin
Mail
Copy Link
https://www.faros.ai/blog/getting-started-with-developer-productivity-four-engineering-leaders-share-their-advice
Share
Twitter
Linkedin
Mail
Copy Link
https://www.faros.ai/blog/getting-started-with-developer-productivity-four-engineering-leaders-share-their-advice
August 16, 2023

Getting Started with Developer Productivity: Four Engineering Leaders Share Their Advice

Developer productivity is a hot topic and it’s seriously no surprise.

In 2023 JP Morgan Chase planned to spend $15.3B on technology. At startups, engineering represents 25% of staff and 31% of the payroll.

No leader wants to be underutilizing that much capacity, especially when tasked with lowering operating margins in this tough economic climate.

But managing productivity can be hard.

According to HBR, an Engineering Manager at Google may have up to 30 direct reports — too many team members to micro-manage. They simply have to focus on creating “the best environment for engineers to make things happen.”

But how do you broach the topic of productivity and the developer experience?

Whether you’re working at an incumbent focused on transformation and modernization or at a disruptor focused on innovation and grappling with growing pains, some challenges may sound familiar:

“Everyone has a different idea of what productivity is.”

“We don’t know what to build or prioritize to better support our engineers.”

“We don't even know where to start or what to be looking for.”

We hear that a lot, too. So we spoke to four seasoned engineering leaders to gather some tips on how to get started, how to become data-driven, and how to harness overall productivity insights.

When should you start investing in developer productivity?

Mustafa Furniturewala is Senior Vice President of Engineering at Coursera, a global platform for online learning and career development that offers online courses and degrees from leading universities and companies.

Mustafa has seen the company grow from 40 engineers to over 300 engineers in the last eight years.

While Coursera had always invested in developer productivity, the organization’s growth proved the tipping point.

“We had a dedicated team once we grew to about 100 people in Engineering,” Mustafa shared.

At Coursera, the developer productivity team was responsible for building a CI/CD pipeline that provided developers with an automated path to production, with a few key objectives:

  • Keeping deployment time under 30 minutes
  • Maintaining a low change failure rate
  • Meeting availability goals

How does Mustafa define developer productivity?

“For measuring developer productivity, it’s important to not look at just one signal but rather have a holistic view that looks at developer activity but also other important metrics like developer satisfaction and the efficiency of flow of information in the organization.”

What is the role of data in developer productivity?

Vitaly Gordon is CEO and Co-Founder of Faros AI, an enterprise software engineering intelligence platform that enables the transformation to data-driven Engineering Operations.

Before co-founding Faros AI, Vitaly was VP of Engineering at Salesforce and the founder of Salesforce Einstein, the world's first comprehensive enterprise AI platform.

At Salesforce, Vitaly was highly focused on the productivity of the hundreds of engineers he managed. And his team knew it.

Passionate engineers would frequently pitch Vitaly a new technology, product, or process they thought Salesforce should adopt in the name of productivity.

“Without data, I could not separate the great ideas from those that were a waste of time. It was overwhelming,” says Vitaly.

Once Vitaly put in place a software engineering intelligence platform for the Einstein group, things changed. When a developer would pitch him a new idea, he changed his approach. Vitaly would say:

“Tell me what problem you’d be solving and what explicitly would improve. We have metrics, we have visibility, we have data. Think it over and come back to tell me which productivity, health, or performance metric would improve.”

That single question proved transformational. “While 90% of the people wouldn’t come back, 10% were serious about their suggestion and did indeed come back with answers. Those proved to be very good ideas indeed,” says Vitaly.

Do organizations have to re-architect their tools and processes to get good data? Vitaly says no.

“Save your energy for driving improvements, not gathering data,” Vitaly advises.

“With a solution crafted around solid data science, you should not have to change a thing. You can get productivity metrics just the way you are today — without introducing friction and overhead.”

How can you measure developer productivity without turning into “Big Brother”?

Jason Selvidge is VP of Engineering at Striim, a supplier of unified, real-time data streaming and integration for analytics and operations.

Jason recommends measuring productivity at the team level and to never use the metrics to compare and contrast individual engineers.

“My goal as a leader is to ensure we’re moving as fast as we can, meaning we’re maintaining our baseline or improving. At the team level, I like to track cycle time, commits per week, and pull requests per week. If we’re dipping below that, I’d like to understand why and help solve the problem,” says Jason.

Since Jason joined Striim, his software development teams have reduced cycle time by 73 percent.

He started with low-hanging fruit, like automated reminders when tasks were stuck too long in a waiting state. Then, he layered in changes to ways of working, like reducing batch sizes.

“To strengthen our culture, I involve the engineers in selecting which metrics we track and how we can improve them. I bring those questions to the team to create alignment on what’s ok to measure. And then I can provide guidance and suggestions on how to improve, drawing on my management experience,” Jason shares.

Jason’s rule of thumb is to devote 5% of your engineering effort to developer productivity, from the very beginning. “If you don’t start early, you’ll accrue a lot of tech debt and you’ll definitely start feeling the pain as you scale.”

Shubha Nabar has built data products and data science teams at LinkedIn and Microsoft. She met Vitaly Gordon at Salesforce developing the Einstein machine learning platform and joined him to co-found Faros AI.

Like Jason, Shubha thinks developer productivity initiatives are about improving day-to-day work for engineers, not policing them.

“Individual metrics can provide pointers for one-on-one coaching and mentoring. But in the dashboards and scorecards I use for reporting and analysis, I like the smallest unit of measurement to be the team and not individual developers.”

Shubha recalls a time she solved an inefficiency for over 100 engineers, which was estimated to be wasting hundreds of developer hours a week.

“I started picking up on low-level grumblings from our team about how long PR merges were taking. When the grumblings intensified, I dug in. The time spent waiting on dependencies and context-switching were really hurting productivity and their frustration was completely justifiable. It pained me to realize how much time was being wasted.”

Shubha figured out the problem and lobbied the SRE team to optimize the merge process.

“If I’d had the productivity metrics I do now, I could have taken action and unblocked my group much sooner.”

Shubha was recognized by Forbes as one of the top 20 women in AI. Now, as Chief Scientist at Faros AI, she’s bringing all the engineering data sources together in a single solution to support business-critical engineering analytics and automation.

Buy or build developer productivity metrics?

Many engineering leaders wonder if they should start by building their own metrics given their customized environments and use of homegrown tools. They also don’t want to be disintermediated from their data by canned metrics vendors, which address only one slice of the productivity equation.

“The buy vs. build paradigm in this case is built on a false dilemma. Engineering software should be buy and build,” says Vitaly.

Vitaly likens a software engineering intelligence platform to payment technology providers Stripe and Plaid, or customer communication and engagement platforms like Twilio. “You buy the things that are undifferentiated for your organization, like the canonical data model, the integrations, and the common use cases, and you use that foundation to build the experience that is unique to your company.“

A software engineering intelligence platform that’s open, extensible, and built with developers in mind can hit that buy-and-build sweet spot.

Mustafa has a similar view:

“At Coursera we [originally] built out dashboards on Sumo Logic that were error-prone and slow,” says Mustafa. “This is where we decided to pilot Faros AI for an out-of-the-box solution that also provided the flexibility and customizability that we need, and we are now rolling it out to the organization.

“We want data about our systems and processes to be easily available, queryable, and preferably all in one place so that data can be a bigger part of our decision-making processes.”

To get started with developer productivity, contact the team at Faros.ai.

Naomi Lurie

Naomi Lurie

Naomi is head of product marketing at Faros AI.

Connect
Back to Blog Posts
AI Is Everywhere. Impact Isn’t.
75% of engineers use AI tools—yet most organizations see no measurable performance gains.

Read the report to uncover what’s holding teams back—and how to fix it fast.
Read the report
AI Productivity Paradox Report 2025
Discover the Engineering Productivity Handbook
How to build a high-impact program that drives real results.
‍
What to measure and why it matters.

And the 5 critical practices that turn data into impact.
Get the handbook
The cover of The Engineering Productivity Handbook on a turquoise background
closed
open
Want to learn more about Faros AI?

Fill out this form and an expert will reach out to schedule time to talk.

Loading calendar...
An illustration of a lighthouse in the sea

Thank you!

A Faros AI expert will reach out to schedule a time to talk.
P.S. If you don't see it within one business day, please check your spam folder.
Oops! Something went wrong while submitting the form.

More articles for you

Editor's Pick
DevProd
Guides
6
MIN READ

Engineering Team Metrics: How Software Engineering Culture Shapes Performance

Discover which engineering team metrics to track based on your software engineering culture. Learn how cultural values determine the right measurements for your team's success.
August 26, 2025
Editor's Pick
DevProd
Guides
10
MIN READ

Choosing the Best Engineering Productivity Metrics for Modern Operating Models

Engineering productivity metrics vary by operating model. Compare metrics for remote, hybrid, outsourced, and distributed software engineering teams.
August 26, 2025
Editor's Pick
DevProd
Guides
10
MIN READ

How to Choose the Right Software Engineering Metrics for Every Company Stage

Discover the best software engineering metrics for startups, scale-ups, and enterprises. Learn how to choose metrics in software engineering by company stage.
August 25, 2025

See what Faros AI can do for you!

Global enterprises trust Faros AI to accelerate their engineering operations. Give us 30 minutes of your time and see it for yourself.

Request a demo
Salespeak