It’s a familiar scene in engineering teams everywhere: Your small team is juggling multiple priorities, fighting fires while trying to ship new features, and somehow expected to maintain velocity through it all. The CEO wants to know why things aren’t moving faster. The product team is concerned about delivery timelines. And your engineers? They’re wondering why they spend more time in meetings than coding.
If this resonates, you’re not alone. Productivity challenges hit differently when you’re running a small team. You don’t have the luxury of dedicated DevOps engineers, platform teams, or extensive tooling. Every engineer wears multiple hats, and every minute counts.
But being small can actually be a superpower. Small teams can move with agility, adapt quickly, and implement changes that would take months in larger organizations. The key is understanding what productivity really means for your team and adopting practical strategies to improve it.
In this guide, we’ll cut through vanity metrics and “best practices” designed for big tech and focus on what actually works for small teams, including:
- What productivity really means for small engineering teams
- Why traditional productivity metrics often mislead us
- Practical strategies to improve velocity without burning out your team
What Does Engineering Productivity Mean for Small Teams
“How do we measure engineering productivity?”
I hear this question constantly from engineering managers, often followed by suggestions like counting pull requests, tracking lines of code, or monitoring GitHub activity. But here’s the uncomfortable truth: most of these metrics measure activity, not actual productivity.
Think about it: Have you seen a team with tons of commits and PRs that wasn’t shipping meaningful features? Or a team that seemed “quiet” on GitHub but delivered high-impact work? The disconnect happens because we measure what’s easy to count, not what actually matters.
For small teams, this distinction is critical. With limited resources, you can’t afford to optimize for the wrong things. Instead, ask: What does meaningful productivity look like for our specific context?
A five-person startup team building an MVP has different productivity needs than a small team maintaining a mature product. Consider these scenarios:
For a startup team, productivity means:
- Shipping features that validate product-market fit
- Moving quickly and pivoting based on user feedback
- Managing technical debt just enough to maintain velocity
For a maintenance team, productivity means:
- Minimizing system incidents and customer escalations
- Improving system reliability and performance
- Efficiently handling bug fixes while modernizing the codebase
Neither definition is wrong—they’re optimized for different situations. Problems arise when teams apply someone else’s definition of productivity without considering their own needs.
How to Discover What Productivity Means for Your Team
Here’s a simple exercise I use with teams:
- What would make next quarter our most successful yet? Be specific.
- What are the top three things preventing that from happening?
- If we could measure only three things to track progress, what would they be?
The answers often surprise people. One team I worked with obsessed over sprint velocity, but their real issue was production incidents caused by rushed deployments.
The goal isn’t a perfect metric—it’s a definition of productivity that helps your team make better decisions. And as your team and product mature, that definition should evolve.
Why Should Small Engineering Teams Measure Productivity?
Productivity metrics often get a bad reputation, especially in software development. Too often, they’re used for executive-level reporting rather than improving workflows. But when done right, measuring productivity isn’t about tracking output—it’s about enabling teams to work smarter, reduce frustration, and build better products.
For small teams, measurement is even more critical. Without layers of redundancy, efficiency is a top priority. Here’s why it matters:
1. Identifying Bottlenecks and Improving Workflows
Every team encounters roadblocks—slow reviews, unclear requirements, or unnecessary back-and-forths. By tracking key workflow metrics, small teams can pinpoint where work gets stuck and fix inefficiencies. Instead of relying on gut feelings, teams can make informed adjustments to streamline processes.
2. Making Data-Driven Decisions About Tools and Processes
The software world is full of tools promising to boost productivity, but without data, it’s hard to know which actually help. Measuring productivity allows teams to assess whether a new tool improves efficiency or just adds complexity.
3. Tracking Progress and Celebrating Successes
Small teams move fast, juggling multiple responsibilities. Without measurement, progress can feel invisible. Tracking productivity metrics helps teams visualize achievements, recognize milestones, and celebrate wins—crucial for morale and motivation.
4. Improving Predictability and Planning
Small teams can’t afford surprises. Measuring productivity helps improve predictability, enabling better sprint planning and realistic deadlines. When teams understand their own velocity, they can manage stakeholder expectations and avoid last-minute crunches.
5. Empowering Teams with More Control Over Their Work
The best productivity measurements put decision-making power in the hands of the team. Instead of relying on top-down directives, teams can use data to refine workflows, advocate for necessary changes, and take ownership of their efficiency.
Why You Should Not Rely on Traditional Productivity Metrics (and What to Use Instead)
For years, software teams have relied on traditional productivity metrics like lines of code (LOC), pull requests (PRs), and commit counts. While easy to track, these often provide misleading insights. Here's why:
- Lines of Code (LOC): More code doesn’t mean better productivity—efficient developers often write less code by simplifying solutions. Measuring LOC encourages bloated, inefficient code.
- Pull Requests and Commits: Frequent PRs and commits don’t guarantee meaningful progress. A developer making fewer but more impactful changes is often more productive.
- Task Completion Rate: High task completion doesn’t reflect complexity or value. Teams may prioritize easy wins over meaningful work
What to Measure Instead
Small teams should focus on value-driven metrics that reflect actual progress:
- Cycle Time: The time it takes for a task to move from start to finish.
- Deployment Frequency: How often changes successfully reach production.
- Lead Time for Changes: The time from code commit to production deployment.
- Mean Time to Recovery (MTTR): How quickly the team restores service after a failure.
- Customer Satisfaction: Feedback on delivered features.
Tracking these doesn’t have to be complicated. Use Kanban or Scrum boards to visualize cycle times, use CI/CD pipelines, and collect user feedback.
How to Boosting Velocity in Your Small Engineering Team
Now that you're tracking the right metrics, it's time to put them to use. Boosting velocity in a small team isn't about demanding more; it's about working smarter. Here are some practical strategies:
1. Empowerment and Autonomy: Micromanagement kills productivity. When team members feel trusted and empowered, they're more engaged, motivated, and productive. Give your small team more ownership over their work. Let them choose their tools, define their processes, and make decisions about how they approach tasks. This sense of autonomy fosters ownership and accountability, leading to increased efficiency and better results.
2. Optimizing Workflow: Waste is the enemy of velocity. Identify and eliminate unnecessary steps in your workflow. Consider implementing Kanban or another workflow management system to visualize your work, track progress, and identify bottlenecks. Reduce handoffs and interruptions, which can significantly disrupt flow and increase context switching.
3. Reducing Cognitive Load: Context switching is a productivity killer. When team members are constantly juggling multiple tasks and switching between different projects, their cognitive load increases, leading to errors, delays, and burnout. Prioritize tasks effectively, ensure clear communication, maintain thorough documentation, and create focused work periods where team members can concentrate without distractions. Consider implementing "deep work" sessions or minimizing meetings to allow for uninterrupted work.
4. Fostering Psychological Safety: Innovation and learning thrive in a psychologically safe environment. When team members feel comfortable taking risks, sharing ideas, and admitting mistakes, they're more likely to experiment, learn, and improve. Create a culture of open communication and feedback, where everyone feels valued and respected. Implement blameless postmortems to learn from failures without assigning blame.
5. Managing Technical Debt: Technical debt is like a credit card: it might seem helpful in the short term, but it can cripple you in the long run. Ignoring technical debt will eventually slow you down and make it harder to deliver new features. Prioritize refactoring and code quality, and dedicate time for addressing technical debt. This might seem like a short-term investment, but it will pay off significantly in the long run.
6. Continuous Improvement: Productivity improvement is an ongoing journey, not a destination. Regularly review your metrics, hold retrospectives, and experiment with new approaches. Use A/B testing for process changes to see what works best for your team. Embrace the iterative nature of improvement and be willing to adapt and evolve your processes over time.
Conclusion
Measuring productivity isn’t about tracking arbitrary numbers—it’s about enabling small teams to work more efficiently and effectively. By focusing on meaningful metrics, optimizing workflows, and fostering a culture of continuous improvement, teams can boost velocity without burning out.
Productivity is a journey, not a destination. Keep experimenting, adapting, and finding what works best for your team.