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See part 1, which discusses setting the stage for effective collaboration and part 2, which discusses managing healthy projects. Seasons leaders understand that wrapping up a project involved much more than choosing what to work on next. Part of the impact for the team and the broader org is the learnings you gained. Every initiative, whetherContinue reading “Metawork for Engineering Leaders Part 3 – Wrapping Up Projects”

See part 1, which discusses setting the stage for effective collaboration After the team agrees on a goal and formulates an initial plan of attack, a team leader’s primary value is tracking project status, supporting people in achieving their goals, and helping the team course correct as necessary. The following is a non-comprehensive list ofContinue reading “Metawork for Engineering Leaders Part 2 – Managing Healthy Projects”

When I first started growing tech leads, my biggest challenge was giving people sufficient support without being overbearing and diminishing their perceived ownership of their project. So I devised a list of questions to ask my TLs to give them a better sense of my expectations and help me quickly assess the health of theContinue reading “Metawork for Engineering Leaders Part 1 – Setting the Stage for Effective Collaboration”

Early on in my career, I assumed that being a great leader is about having the right answer all the time. After seeing several projects succeed and fail and reflecting on the reasons, I started to see things much differently. The most successful projects are almost never headed in the right direction and set upContinue reading “The Importance of Healthy Iterative Loops”

Data literacy is one of the more underrated parts of the software engineering skillset. When you’re dealing with a complex, dynamic, evolving system, being able to reason about data is at times more important than institutional knowledge, which tends to become outdated. Understanding a single library or subsystem really well often isn’t good enough. AndContinue reading “Turning Production Performance Data Into Wisdom”