Tech leadership: lessons learnt

As engineers we often prefer hard facts over the fluffy stuff. As a manager most of our impact is through soft skills. How does an engineering manager find balance the between the worlds of data-driven team metrics and the people skills required to get the best from their team.

In this talk, Dale shares some of his hard-won lessons managing an ever growing engineering team using a variety of processes like kanban and lean product development, and tools like metrics, coaching, autonomy and guard-rails.

Dale is co-founder and CTO of Nomanini, a local fintech startup that helps informal merchants across Africa thrive.

Transcript

1. Dale Humby, Nomanini dale@nomanini.com Tech leadership Lessons growing a dev team
2. Dale Humby CTO co-founder Nomanini Cash payments In Africa
4. About this talk
6. What is a start-up? “A startup is a human institution designed to deliver a new product or service under conditions of extreme uncertainty. Eric Ries http://www.startuplessonslearned.com/2010/06/what-is-startup.html
7. Why have an engineering team?
8. Goals of an engineering team Respond to rapidly changing competitive landscape Provide a stable, reliable, secure service to our customers
9. What is the output of a manager?
10. What is the output of a manager? Output of their team + Output of the teams they influence
11. How do you increase output? + Motivation + Skill
12. The 1st lever: Motivation
13. The 1st lever: Motivation
15. Scrum: 1 month sprints
16. Scrum: 2 – 1 week sprints
17. Scrum (2011)
18. 1 story sprints…?
19. Kanban
20. Feedback loops
21. Kanban Dev team promise (SLA) Work will be deployed to production within 1 week of starting
26. SLA
27. Importance of Work in Progress (WIP) limits
32. Maslow's Hierarchy of Needs
33. The 2nd lever: Increasing skills
34. Task relevant maturity Low Task oriented • What • When • How Medium High Individual oriented • Emotional support • Encouragement Objective oriented • With monitoring
35. Complexity, Uncertainty, Ambiguity Time Complexity, Uncertainty, Ambiguity “Junior”
36. Complexity, Uncertainty, Ambiguity Time Complexity, Uncertainty, Ambiguity “Junior” “Mid”
37. Complexity, Uncertainty, Ambiguity Time Complexity, Uncertainty, Ambiguity “Junior” “Mid” “Senior”
38. Complexity, Uncertainty, Ambiguity Time Complexity, Uncertainty, Ambiguity “Junior” “Mid” “Senior”
39. Complexity, Uncertainty, Ambiguity Time Complexity, Uncertainty, Ambiguity “Junior” “Mid” “Senior”
40. Complexity, Uncertainty, Ambiguity Time Complexity, Uncertainty, Ambiguity “Junior” “Mid” “Senior”
41. Complexity, Uncertainty, Ambiguity Time Complexity, Uncertainty, Ambiguity “Junior” “Mid” “Senior”
42. Complexity, Uncertainty, Ambiguity Time Complexity, Uncertainty, Ambiguity “Junior” “Mid” “Senior”
43. Complexity, Uncertainty, Ambiguity Time Complexity, Uncertainty, Ambiguity “Junior” “Mid” “Senior”
44. Increasing skills • Pair programming, Code reviews • Self study • Coaching, 1-on-1’s • 360’s / peer review • Skills assessment • Medium-term career development • Formal courses
46. 3rd lever: Decentralised control Rapid response
47. 3rd lever: Decentralised control Rapid response Alignment more difficult
48. 3rd lever: Decentralised control Rapid response Alignment more difficult • Focus on the outcome: Why not how/what • One main effort • Consistency of decision making • Trust through experience
49. Key takeaways • Output of a manager = Output of their team + output of neighbouring teams • Tools ◦ Increase motivation: Build a racetrack: Metrics ◦ Increase skills: Tailored mentorship program • Decentralised control works well for extreme uncertainty
50. Books worth reading Thank you dale@nomanini.com
51. Innovation http://web.mit.edu/6.933/www/Fall2000/teradyne/clay.html
52. Complexity, Uncertainty, Ambiguity Time Complexity, Uncertainty, Ambiguity “Junior” “Mid” “Senior”