
Coalesce.io
How Coalesce grew code review throughput 50% with a smaller team
120+
Data Modeling
5
mins read
150 → 225
Weekly code reviews completed after rebalancing
35%
Share of reviews previously carried by three engineers
20-30%
Cycle time reduction across teams within two quarters of focused improvement
Span gave us data to back up our assumptions and the insight to correct them when we were wrong.

Mike McCune
Mike McCune
Coalesce.io is a data transformation platform with an engineering organization of roughly 40 people distributed across North America, New Zealand, and Europe.
Mike McCune, VP of Engineering at Coalesce, oversees the engineering organization. Over the past two years, he has grown the team from 20 to 40 engineers. Throughout that period, his focus has been on scaling Coalesce’s engineering practice, including eliminating many of the structural risks that hide inside fast-growing organizations.
A hidden concentration risk in Coalesce's review practice
For most engineering organizations, code review is the connective tissue of a healthy team. But it’s often one of the least openly visible.
That distribution was exactly the problem at Coalesce. Coalesce's leadership team suspected the review load wasn't evenly distributed, but suspicion isn't enough to act on. "Without tools like this, it ends up being a lot of word of mouth, recency bias, and things you infer from Slack discussions or meetings, which is a very limited scope of what a team is actually doing," Mike notes.
Span showed that about 35% of all code reviews were primarily conducted by three engineers. This was a continuity risk. With teams distributed across the world, the org couldn't afford a single point of failure in its review practice.
Span made review distribution legible
Span quickly demonstrated a different category of value far beyond what spreadsheets, Jira queries, and the previous tool could provide.
Because Span connects to GitHub, Jira, and the rest of the systems Coalesce's engineers already work in, it surfaces patterns that would otherwise stay hidden: who's reviewing what, where bottlenecks form, how work is distributed across individuals and teams.
While Coalesce’s leadership team had their suspicions about how heavily the review burden was concentrated among the team, Span gave them a quantifiable way to track whether redistribution efforts were actually working.
A more resilient review practice and a higher-throughput team
Redistribution tracked over time
Once Span quantified how concentrated code review was among the team, Mike recognized that the pattern as unsustainable and began deliberately redistributing review work across the broader org. Span's visibility made the rebalancing measurable: rather than relying on impressions of whether the load was actually shifting, they could track whether the same few engineers were quietly absorbing the work again, or whether the redistribution was holding.
This redistribution was viewed as a structural investment in the resilience of the org. More engineers reviewing more code often meant more knowledge sharing, more growth opportunities for mid-level engineers, and a review practice that didn't depend on any one person's availability.
As a result, weekly code review throughput rose from roughly 150 reviews per week in 2025 to 225 in 2026. This was a 50% increase achieved with a smaller team thanks to a more balanced distribution of work.
Cycle time reduced 20–30% across teams
The same visibility that surfaced the review imbalance also surfaced bottlenecks elsewhere in the development workflow. Span made one long-suspected drag legible: Coalesce's CI/CD pipeline was costing the team meaningful engineering time. "We knew this in our hearts, because we felt it as developers, but now we knew how much time the team was being spent babysitting our CI pipelines,” explained Mike.
Coalesce dedicated two quarters of focused attention to cycle time, using Span to identify where work was getting stuck — whether in review cycles, CI runs, or merge queues — and to track improvement over time. The result was a 20–30% reduction in cycle time across the teams where the focus was applied. Combined with the throughput gains from the review redistribution, the team was moving meaningfully more work through the system at a faster pace.
“We spent two quarters paying genuine attention to some of the core issues, and we averaged a reduction of 20–30% cycle time across the teams. It was a healthy result, and it has stuck.”
Mike McCune, VP of Engineering
Looking Ahead
Coalesce continues to use Span as the operating layer for its engineering organization, with monthly progress reports drawing on Span data to keep leadership informed of what the team is delivering and where the focus is. Span has also become foundational to how Mike works with his first-time managers, giving them data to anchor feedback and quarterly reviews.
More recently, Mike has shifted much of his day-to-day Span usage to Src, the natural-language interface for querying engineering data. Where he once exported CSVs and ran them through Claude Code or Cursor to generate the formats he needed, he can now ask questions directly and get the data back in whatever shape the moment requires.
Src, which recently launched for all Span customers, is just the latest example of how Span is frequently unveiling new use cases for customers, especially with a wider AI Effectiveness suite rollout on the way.
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