#25 Product & Engineering Wisdom
Tips, advice and insight on storytelling, CI/CD automation and technical literacy.
A free fortnightly email that highlights the relevant tips, advice, and case studies from the world of product and engineering for the SEO community.
Hello new subscribers 👋,
For anyone new to this newsletter this is the fortnightly roundup of posts from the product and engineering community.
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Stay safe and enjoy,
Adam
⚡Post of the Sprint
📟 Storytelling: How I learned to love it - Spotify
Reading Time: 32 mins
Summary: Brendan Marsh, a Product Coach at Organa and Ex-PM at Spotify, tells the tale of his experience at Spotify and why he had to learn to tell stories to climb the career ladder.
The Bottom Line
I always enjoy it when a product manager goes through their experiences and provides a clear case study on how they navigated the chaos of building a product.
In this webinar Brendan Marsh goes through his story of how he made it into Spotify, the changes that happened in the company and how he used story telling to convince leadership to invest in a better desktop app.
In this webinar he even goes through the exact story he told to developers and leadership to get buy-in to improve the desktop app (which was way behind on features compared to mobile).
Brendan even provides techniques and frameworks to help others turn their strategies into compelling stories. For example the Storytelling Canvas or Picture Theory.
Useful for SEO specialists who work in-house who need to tell a story about the neglect of the SEO channel by a company
✨Product
📟 How to Excel in Tech Without Learning to Code
Reading Time: 10 mins
Summary: In this article, Justin Gage discusses why technical literacy matters for non-technical stakeholders and how to improve your own technical literacy.
The Bottom Line
This is a great article for those who want to better understand the learn to code vs technical literacy debate.
What I like about this article is that it breaks down why it is important to be technically literate, for example for product managers, marketers and sales.
It then provides suggestions on how to improve your technical literacy including make a plan to learn, speaking to developers and find influencers and experts online (internet is full of rubbish but there is gold out there).
Useful for any SEO who wants to improve their technical literacy.
⚙️Engineering
💻What Silicon Valley "Gets" about Software Engineers that Traditional Companies Do Not
Reading Time: 10 mins
Summary: Gergely Orosz's article covers how software companies treat software engineers (developers) compared to traditional companies.
The Bottom Line
This is a great article that mirrors many of the insights I found in my 5 key Lessons from interviewing in-house SEOs newsletter. However, the article mentions some interesting differences in how software companies treat developers vs traditional companies. These differences included:
Allow them to be curious problem solvers, not mindless resources
Create internal data, code, and documentation transparency
Exposure to the business and to business metrics
If you are an SEO who works with development teams and wonder how to work with them effectivley I highly recommend giving this a read.
💻 Handling Flaky Tests at Scale - Slack
Reading Time: 15 mins
Summary: Arpita Patel on the Mobile Developer Experience Team (DevXp) talks about Continuous Improvements (CI) changes at Slack and the improvements they have made to handle the rate of test failures due to flaky tests.
The Bottom Line
This is a great blog post which really digs into an issue that many development teams face: flaky (bad) tests. It is part 1 of 2 blog posts in a series.
Flaky automated tests can cause problems because they don’t pick up bugs or defacts in code which cause problems in user experience on the website.
What I quite enjoyed about the post is that Arpita really shows both the tests and continous learning the team used to explore the problem. It shows that even though the problem seems simple (removing flaky tests) when you actually dig into a solution it starts to become very complex.
If you want to see how a large company is trying to solve a common CI problem this is an interesting one (even had data and experiments to show learning).
💻 Beyond Matrix Factorization - Yelp
Reading Time: 20 mins
Summary: Srivathsan Rajagopalan a Machine Leaner Engineer at Yelp, discusses the generalized user to business recommendation model their team uses machine learning to power Yelp products (push notifications, email, the home feed, etc.).
The Bottom Line
This might not be for everyone but I do enjoy reading how a development team thinks and creates a solution in the back-end that powers so many front-end products.
This is a great blog post to better understand how tech teams approach and think about solutions to solve a problem (bit technical).
That's it! Please share this newsletter if you find it interesting 👇.