Scaling Up without Slowing Down

Andrew Murphy and Inga Pflaumer walk through the hard truths of scaling engineering organizations without losing velocity. They start by framing scaling as a why-driven exercise: you only add people, systems, or complexity when a clear business outcome demands it. The conversation quickly turns to metrics, emphasizing that metrics must be open, collaborative, and tied to the team's own goals, not a surprise in a one-on-one. Inga illustrates this with concrete examples like measuring PR reviews, cycle time, and incident resolution, and warns that metrics become noise when they're imposed top-down.

The pair dissect what scaling really means, debunking the myth of linear growth. Adding thirty engineers to a team that previously relied on ad-hoc conversations creates a flood of context-switching and endless meetings. They argue for processes that scale and for hiring based on "people-shaped holes"-specific skill gaps-rather than arbitrary headcount targets. Inga's advice to be upfront about future role expectations during interviews helps self-select candidates who are comfortable with the evolution of their responsibilities.

Culture is reframed as a set of observable behaviors that can be measured and reinforced. The speakers describe how celebrating certain behaviors, like thorough PR reviews, directly shapes the engineering culture. They also caution against transplanting a foreign culture into a growing team; instead, leaders should identify which cultural practices support the next stage of growth and deliberately let go of those that no longer add value.

Hiring conversations are presented as a two-way street. Honest dialogue about future backend needs, flexibility, and the kind of impact a candidate wants to have leads to better fits and reduces turnover. The discussion also covers signals of over- or under-hiring, recommending 30/90/120-day plans and using both objective data and subjective sentiment surveys to gauge team health during scaling phases.

Finally, Andrew and Inga stress that data only becomes information when it's linked to business goals. Combining subjective metrics-engineer sentiment about deliverables-with objective data creates a richer context for decision-making. For technical leaders, the takeaway is clear: scale with purpose, measure transparently, and let culture evolve through concrete, measurable behaviors.