How to Build a Top Tech Company

Paulo Silveira
6 min readFeb 28, 2021


What is ‘How To Build a Top Tech Company’?

This is a high-quality technical content aggregator made for everyone interested in building successful technology companies.

Why am I doing ‘How To Build a Top Tech Company’?

I’m quoting Steve Jobs on this one:

Creativity is just connecting things. When you ask creative people how they did something, they feel a little guilty because they didn’t really do it, they just saw something. It seemed obvious to them after a while. That’s because they were able to connect experiences they’ve had and synthesize new things.

I won’t create something new or solve hard problems if I’m only thinking about ‘How to Build a Neobank’.

And the reason they were able to do that was that they’ve had more experiences or they have thought more about their experiences than other people. Unfortunately, that’s too rare a commodity. A lot of people in our industry haven’t had very diverse experiences. So they don’t have enough dots to connect, and they end up with very linear solutions without a broad perspective on the problem. The broader one’s understanding of the human experience, the better design we will have.

I’m doing this because I want to improve my chances of creating something new and solving hard problems by having enough ‘tech-company-dots’ to connect.

How may I help ‘How To Build a Top Tech Company’?

I’m presenting a ‘to-be-curated’ reading list at the end of this article. Feel free to read them and recommend new entries to be aggregated. Just comment, reply or something. I’m also available on LinkedIn, if you want to send me a connection request: Paulo Silveira.

Data & Analytics

Decision Scientists at Gojek — The Who, What, Why (Gojek)

Building A Data Science Product in 10 Days (Instacart)

An introduction to Decision Engineering (Grubhub)

How We Improved Data Discovery for Data Scientists at Spotify (Spotify)

Pinterest Trends: Insights into unstructured data (Pinterest)

How Data Science Helps Power Worldwide Delivery of Netflix Content (Netflix)

Data Science and the Art of Producing Entertainment at Netflix (Netflix)

How Our Paths Brought Us to Data and Netflix (Netflix)

Analytics at Netflix: Who We Are and What We Do (Netflix)

Mythbusting the Analytics Journey (Netflix)

Democratizing data analysis with Google BigQuery (Netflix)

Discovery and Consumption of Analytics Data at Twitter (Twitter)

How we think about data at Coinbase (Coinbase)

So You Have Some Clusters, Now What? (Square)

4 Principles for Making Experimentation Count (Airbnb)

Automated Machine Learning (Airbnb)

How Airbnb Democratizes Data Science With Data University (Airbnb)

Selection Bias in Online Experimentation (Airbnb)

Helping Guests Make Informed Decisions with Market Insights (Airbnb)

Fighting Financial Fraud with Targeted Friction (Airbnb)

Learning Market Dynamics for Optimal Pricing (Airbnb)

How Airbnb is Boosting Data Literacy With ‘Data U Intensive’ Training (Airbnb)

Discovering and Classifying In-app Message Intent at Airbnb (Airbnb)

Empowering Data Science with Engineering Education (Airbnb)

Advice for New Data Scientists (Airbnb)

Using Machine Learning to Predict Value of Homes On Airbnb (Airbnb)

Data Quality at Airbnb (Airbnb)

Visualizing Data Timeliness at Airbnb (Airbnb)

Top Five Lessons from Running A/B Tests on the World’s Largest Professional Network (LinkedIn)

Who Are You? A Statistical Approach to Protecting LinkedIn Logins (LinkedIn)

Making Hard Choices: The Quest for Ethics in Machine Learning (LinkedIn)

Behind “Big Data” and “AI”: Elements of Modern Data Science (LinkedIn)

An Introduction to AI at LinkedIn (LinkedIn)

LinkedIn Sales Insights: Quality data foundations for smarter sales planning (LinkedIn)


Good bug reports lead to great bug fixes (Gojek)

How We Do What We Do at GOJEK (Gojek)

Spotify engineering culture (part 1) (Spotify)

Spotify engineering culture (part 2) (Spotify)

Building a technical career path at Spotify (Spotify)

How We Use Golden Paths to Solve Fragmentation in Our Software Ecosystem (Spotify)

Lessons learned in incident management (Dropbox)

Your System is not a Sports Team (Dropbox)

Embracing papercuts (Dropbox)

Don’t lead by example (Dropbox)

A Pinterest Engineering guide to technical interviews (Pinterest)

Three-day no-meeting schedule for engineers (Pinterest)

Building a faster mobile web experience with AMP (Pinterest)

Python at Netflix (Netflix)

How we built Twitter Lite (Twitter)

The Infrastructure Behind Twitter: Scale (Twitter)

Surviving the Brazilian Reality TV Surge that Pushed Twitter to the Edge (Twitter)

Rebuilding Twitter’s public API (Twitter)

Manhattan software deployments: how we deploy Twitter’s large scale (Twitter)

What are Coinbase’s Engineering Principles? (Coinbase)

You need more than one AWS account: AWS bastions and assume-role (Coinbase)

Remote Engineering at Coinbase (Coinbase)

How One Eng Team Works (Coinbase)

How To Be a More Influential Engineer (Square)

Soft-skills Reading List (Square)

Messaging Sync — Scaling Mobile Messaging at Airbnb (Airbnb)

React Native at Airbnb (Airbnb)

React Native at Airbnb: The Technology (Airbnb)

Introducing Pipelines to Airbnb’s Deployment Process (Airbnb)

From Good to World-Class: What Makes Software Engineers Excel at their Craft (LinkedIn)

Scaling Decision-Making Across Teams within LinkedIn Engineering (LinkedIn)

Managing documentation at scale (LinkedIn)

Rebuilding messaging: How we bootstrapped our platform (LinkedIn)

Rebuilding messaging: How we designed our new system (LinkedIn)

How LinkedIn turned to real-time feedback for developer tooling (LinkedIn)

The Modular Monolith: Rails Architecture (Root)

Deconstructing the Monolith: Designing Software that Maximizes Developer Productivity (Shopify)


How We Use Golden Paths to Solve Fragmentation in Our Software Ecosystem (Spotify) on Aug 01, 2021.



Paulo Silveira

product manager & software enthusiast