The Rare Insight Top Tech Companies Have About Engineering Managers

Most engineering managers [EMs] are bad and aren't much more than glorified, overpaid whip-crackers. Here's what the world's best tech companies realize about engineering management that other companies don't.

Here are the core points:
- **Good EMs think long-term** - It's really easy to burn out the team for short-term gains, just working them harder and harder so everything ships quickly. However, this isn't hard to do and sacrifices the future of the team. Teams run this way eventually reach a state of weak productivity due to high turnover and completely drained engineers. Good EMs strive to operate their teams in a sustainable way, taking care of their engineers while also pushing for velocity.
- **Good tech companies hold EMs accountable** - In particular, EMs are expected to uphold the overall health and integrity of their teams. Companies like Meta explicitly score managers on how well they are able to give their engineers growth opportunities, preserve their work-life balance, and shield them from thrash so they have the focus to execute.

Related resources:
- [[Masterclass] What Software Engineers Should Look For In Their Engineering Manager](https://www.jointaro.com/lesson/oeY7ncZhzJ5NO6dij5e7/masterclass-what-software-engineers-should-look-for-in-their-engineering-manager/) - The full video where this clip was taken from
- [How Do Effective Engineering Leaders Help Their Teams Achieve More?](https://www.jointaro.com/lesson/xGJDCG7VL4hcZaq5XL92/how-do-effective-engineering-leaders-help-their-teams-achieve-more/)
- ["How do I figure out whether I should stay as an IC engineer or explore management as an EM?"](https://www.jointaro.com/question/Y6qIop9wpOlCYUL0nGs0/how-do-i-figure-out-whether-i-should-stay-as-an-ic-engineer-or-explore-management-as-an-em/)
- ["What are the commonalities and differences between tech leads and engineering managers?"](https://www.jointaro.com/question/c3MLGlkQgOQ6OUHANAxT/what-are-the-commonalities-and-differences-between-tech-leads-and-engineering-managers/)

This Is How Software Engineers Should Initially Learn

- Learn depth-wise initially, and then you can go breadth-wise after you become a very established senior engineer.
- If you don't know what you're passionate about, try a bunch of things out (go for breadth) until you find that passion.
- After finding your passion, go deep on it.
- You need to commit a long period of time to just 1 thing initially, so you start learning fundamentals as opposed to tech stack-specific tactical pieces.
- *You will eventually realize that all tech stacks are the same in many ways* - All languages and tech stacks share a common set of fundamentals. You will not get to this state if you switch stacks every year.

Related resources:
- [How The Best Software Engineers Learn A Tech Stack - The Lego Model](https://www.jointaro.com/lesson/9cVK5zsAQstbTCc1wrWx/how-the-best-software-engineers-learn-a-tech-stack-the-lego-model/)
- ["How do you get more independent?"](https://www.jointaro.com/question/FUP3gHb7rv9CXc84LaKn/how-do-you-get-more-independent/)
- ["How can I write great code faster?"](https://www.jointaro.com/question/ciE9a2TZ7rZNyYP8HbeJ/how-can-i-write-great-code-faster/)

Why Your Manager Sucks, And What You Can Do As A Software Engineer

Rockstar Software Engineer Story: SW2 -> Principal In Just 4 Years

- An engineer joined Mixpanel as a level 2 Software Engineer, and received 4 promotions in 4 years to become a Principal Tech Lead Manager (TLM).
- Key to his success was the ability to make rapid progress on one of the company's top priorities: reduce infrastructure spend.
- He had a hypothesis that the majority of the cost incurred came from a few customers where the economics were backward for Mixpanel.
- Rather than solving a gnarly technical problem, the engineer worked with these customers to change their use of compute resources, resulting in massive savings for the company.

For more resources about becoming a rockstar engineer:

- ["What's the core insight behind quick growth?"](https://www.jointaro.com/question/nJ762p255cGyuth36H6w/whats-the-core-insight-behind-quick-growth/)
- ["What's the difference between a low performer software engineer and a high performer?"](https://www.jointaro.com/question/nMxx2EMVIlJbl1oK4SGu/whats-the-difference-between-a-low-performer-software-engineer-and-a-high-performer/)
- [Mentoring Junior SWEs \[E3\] to Senior \[E5\] In Just 2.5 Years At Meta](https://www.jointaro.com/lesson/EX9563YnxcJb62cCmetg/case-study-mentoring-junior-swes-e3-to-senior-e5-in-just-25-years-at-meta/)

What Makes A Truly Great Tech Lead - A VP of Engineering's Perspective

Former Mixpanel VP of Engineering David Pan (also ex-Twitter, ex-Amazon, ex-Microsoft) shares his wisdom on what it really means to be a tech *lead*, shipping projects effectively and leaving the team in a better place in the process.

- **Overview:** Tech leads are extremely valuable. They allow a team to multi-thread and execute on multiple projects at once, not requiring the engineering manager to babysit everything.
- **Understands the business context:** Great tech leads have strong product understanding and realize that software needs to be built in different ways based on the situation.
- **Project risk shrinks over time:** Great tech leads are constantly looking for ways to de-risk their projects over time. As things get closer to the ship date, the project actually becomes less scary!
- **Level up the team along the way:** Great tech leads figure out ways to leave the team in a better place than it was before the project.
- **If you're looking to become a great tech lead**: Get to know your PM, assess risk proactively and tackle the hardest items first, and find opportunities to level up your teammates.

You can connect with David here: [David Pan's LinkedIn](https://www.linkedin.com/in/davepan/)

If you want to learn more about becoming a great tech lead, check out the following resources:

- [\[Course\] Tech Lead Blueprint: Role, Skills, and Strategies](https://www.jointaro.com/course/tech-lead-blueprint-role-skills-and-strategies/)
- [Becoming A Tech Lead Again In Just 1 Month After Joining Robinhood From Meta - Case Study From Alex](https://www.jointaro.com/lesson/X8vpfJooDpsX915ouC00/case-study-becoming-a-tech-lead-again-in-just-1-month-after-joining-robinhood-from-meta/)
- ["How do I lead without authority?" - Staff Software Engineer \[E6\] at Meta](https://www.jointaro.com/question/BWv3hFSELbn59mXJbkNB/how-do-i-lead-without-authority/)
- ["How can I hit the ground running as a tech lead?" - Senior Software Engineer \[SDE 3\] at Amazon](https://www.jointaro.com/question/MiEPhSkzr8TRRSMRkrNQ/how-can-i-hit-the-ground-running-as-a-tech-lead/)

The #1 Rule Of Negotiation - How To Avoid Giving A Number

- Negotiation is a dance: Know your role within it and have a script.
- There's an asymmetry here:
  - Recruiters are talking to candidates and negotiating every day. They come in very prepared and have a script.
  - You will change jobs every 2-3 year - It's a rare event for you to have this conversation. To level the playing field, you should have a script too.
- If they ask for a number or range, say *"I'm more concerned with understanding if this opportunity is right for me."* This is polite while positioning yourself in a mutual respect scenario.
- If they refuse to move forward within a number, you can try:
  - "What are typical compensation bands for this role?"
  - "I'm a strong performer in my current role and expect to be promoted soon, so I'm not sure what's fair."
- If they require a number, put a placeholder and say you'll get back to it later
- Asking for a number is generally a predatory way for companies to take advantage of candidates and lowball them.
  - This is why in California, it is illegal for employers to ask your prior compensation information

Related resources:

- [\[Course\] The Insider's Guide To Negotiating Your Tech Salary And Compensation](https://www.jointaro.com/course/the-insiders-guide-to-negotiating-your-tech-salary-and-compensation/)
- ["Negotiation - done via phone call or email? Modes and stating a number?"](https://www.jointaro.com/question/ggYVsodBllba5RmYrOtv/negotiation-done-via-phone-call-or-email-modes-and-stating-a-number/)

Why You Should Start Your Career At A Big Tech Company

- **There's no universal right answer to which company works best for you** - At the end of the day, you should figure out what you care about and what your goals are and work backwards from there. It's entirely possible that Big Tech isn't a good fit for you.
- However, in a vacuum, you should start your career at a Big Tech company if you can. The main reason is that Big Tech is generally quite good at adding a layer of consistency to your career.
- You will probably learn a lot about writing good, scalable code as these companies know how to build massive systems and hire smart people.
- Your compensation will be very competitive, making it easier to negotiate proper pay and level at future jobs.
- You will probably develop a decent suite of fundamental soft skills as you need to learn how to work with many stakeholders to succeed at Big Tech.

Promotion Is Underrated And The True Foundation Of Your Pay And Career

Here are the core points from the video:

- In a lot of companies, promotion isn't meaningful, both in terms of growth and pay increase. *However, promotion is extremely powerful when you're working at a top-tier company like FAANG.*
- In a good environment, promotion is the most organic option to increase your total compensation as it means that your actual skill and increased pay are actually correlated with one another.
  - If you get promoted at a FAANG/Big Tech company, this sets the foundation for future opportunities as you will now be put at this level at least 95%+ of the time. For example, if you become a senior engineer \[L5\] at Google, it is very unlikely that your next company will put you below senior.
  - It's also crucial to remember that at FAANG/Big Tech, the expectations are quite real and quite high. By getting promoted to your level, this means that you are actually able to perform at this level, as opposed to the scenario where you negotiated an up-level via a job switch where you might be dangerously over-leveling yourself.
- *The problem with doing the interview prep and job switching grind is that you're not actually growing as a software engineer during that time.* There is a huge disconnect between interview skill and actual skill, especially with data structures & algorithms (DSA).

Additional promotion resources:

- [Nail Your Promotion: Senior To Staff (L5 -&gt; L6)](https://www.jointaro.com/learning-paths/nail-your-promotion-senior-to-staff-l5-to-l6/)
- [Nail Your Promotion: Mid-Level To Senior (L4 -&gt; L5)](https://www.jointaro.com/learning-paths/nail-your-promotion-mid-level-to-senior-l4-to-l5/)
- [Nail Your Promotion: Junior To Mid-Level (L3 -&gt; L4)](https://www.jointaro.com/learning-paths/nail-your-promotion-junior-to-mid-level-l3-to-l4/)

How Base Salary Becomes Insignificant - Tech Compensation Archetypes

**Equity** is the primary reason that tech compensation is so high. Equity means your employer gives you ownership in the company as a portion of your compensation. The idea is that this incentivizes you to work harder to have the company perform well.

For most of Senior+ roles in US-based companies, equity will begin to dominate your pay. (This is the hallmark of a top tech company.) Some examples:

- **For mid-level roles**, the equity in a company could be something like $20K - $100K / year. Senior engineers at top companies start to make almost an equal amount in stock as they do in base salary (cash comp).
- **For very senior roles**, the majority of the total compensation comes from equity. e.g. at the Director level, the ratio is around 2:1, so their base salary may be $270K/year, and their equity could be $500K/year.
- Compare this to a non-tech, old-school company like General Motors. The average pay for a Director of Engineering is $280K, which is great, but you could easily make that amount much earlier in your career within Big Tech. The reason for this gap is because General Motors is a lot more stingy when it comes to giving out stock to their employees.

What Makes Up Total Compensation (TC) In Tech And Which Parts Matter

This video outlines the most important components of compensation in the tech industry and emphasizes how to assess and prioritize them effectively—especially when evaluating job offers or planning a negotiation.

- **Total compensation (TC)** typically includes base salary, equity, and bonuses, but many companies also add less impactful perks like free meals, fitness stipends, or game rooms.
- **The most meaningful and negotiable parts of compensation** are base salary, equity (like RSUs), and signing bonuses. These are the components with the greatest financial impact and room for movement in offer discussions.
- **Perks like catered lunches or company offsites** may sound appealing but usually amount to minor financial value—typically just a few hundred dollars a month—and shouldn’t drive decision-making.
- **Benefits such as 401(k) matching and paid time off** can be substantial, especially at top-tier companies, but are generally standardized and not negotiable between candidates.
- **When evaluating an offer**, focus on the components that both materially affect take-home pay and can be influenced during negotiation: base, bonus, and especially equity.

How To Get Real Feedback So You Can Actually Improve

Core points:

- Let the other person know that you treat all feedback as a gift and really appreciate constructive criticism.
- Schedule a 1:1 with crucial people in your org/team to get feedback. It can be every 1 to 3 months. Make it clear that it's a feedback meeting.
- It's important to understand the feedback giver's perspective. Nice people, those you are (hopefully) working with, are going to hold back often by default to shield you. This is why you need to make it clear that you really appreciate honesty and transparency.
- Feedback is crucial towards promotion.

Related resources:

- ["I got some pretty "unspecific" feedback. How can I sharpen it to make more actionable?"](https://www.jointaro.com/question/gKsY3J4x5rz2zdG2OXYJ/i-got-some-pretty-unspecific-feedback-how-can-i-sharpen-it-to-make-more-actionable/)
- ["I work in a small company with no formal perf review system - How can I get feedback to improve?"](https://www.jointaro.com/question/ZkjOqjW2ou4LhJ0HyIIK/i-work-in-a-small-company-with-no-formal-perf-review-system-how-can-i-get-feedback-to-improve/)
- ["How do I find a proper mentor within my company?"](https://www.jointaro.com/question/53nM0O5iemQD5K5ZQkYE/how-do-i-find-a-proper-mentor-within-my-company/)

A Huge, Often Unused Way Your Manager Can Help With Promotion

- Your manager is critical to your promotion. More specifically, if your manager is not supportive of your promotion case, it is near impossible for you to get promoted.
- You need to create a plan with your manager as soon as the performance review cycle starts to set expectations on what you need in order to be a good candidate for promotion. It should be modal and detail the following:
  - What it means to meet expectations
  - What it means to exceed expectations
  - What it means to totally crush expectations
- Regularly work with your manager to get feedback and understand your progress for this plan

Effectively Learning Data Structures And Algorithms (DSA) For Software Engineer Interviews

Which of these is the core building block of data structures and algorithms problems?

True or false: The most effective way to be prepared for data structures and algorithms interview problems is to memorize the code of each problem's solution line by line.

Why should you comment your solutions to data structures and algorithms problems?

True or false: When you are completely stuck, you should just look at the entire solution in order to learn it.

Why is it healthy to struggle on data structures and algorithms problems?

**Data Structure and Algorithm (DSA)** questions are a core part of the interview for most software engineering roles. These questions, sometimes called "Leetcode problems", are essential to master if you want a reasonable pass rate in your interviews. 

However, so many engineering candidates struggle to truly understand these questions, resulting in lower confidence and worse performance. Here are the best tips when it comes to passing these types of interviews:

- The key to mastering DSA is to learn patterns and not memorize code. The "drill and kill" style of studying is really badly suited for DSA.
- 80% of asked DSA problems will use 1 of ~15 different patterns. So rather than focusing on individual problems, your goal should be to connect the problem to a broader, recurring pattern.
- Examples of patterns: binary search, graph traversal, sliding window, tail recursion. Being able to understand problems of the same type is essential. Being able to *name the pattern* is even better. 
- Extensively comment your solutions, primarily with the key pattern behind the problem. When you get started, it will be painstakingly slow to understand each line of code, and potentially comment it. That's ok! You will feel frustrated, but your goal is to deeply understand the problem and solution, instead of simply solving as many problems as you can.
- When you can't figure out a problem, read 10-20% of the solution and see if you can come up with the entire solution from there. Build the muscle of converting high-level points into fleshed out solutions

Related resources:
- ["Mock Interviews vs. Leetcode?"](https://www.jointaro.com/question/U4kohSE8g5LR7GD9cqYH/mock-interviews-vs-leetcode/)
- [[Masterclass] How To Ace Your Big Tech Interview - Data Structures And Algorithms](https://www.jointaro.com/lesson/V4sTACzfOh22nMVcUV3w/masterclass-how-to-ace-your-big-tech-interview-data-structures-and-algorithms/)
- [[Masterclass] How To Ace Your Big Tech Interview - System Design](https://www.jointaro.com/lesson/J3I9fTW9ulr6T9KjxpqY/masterclass-how-to-ace-your-big-tech-interview-system-design/)
- [How Alex Got Into Meta With 0 Prior LeetCode Experience](https://www.jointaro.com/lesson/ZYiTbN21FsqFxFdpwt5a/how-alex-got-into-meta-with-0-prior-leetcode-experience/)

Having The Proper Mindset For Interviews

True or false: You need to care a lot about your Big Tech interview, because passing it will drastically improve your life. The more prominent it is in your brain, the more likely you are to pass.

True or false: Your input is much more important than the output and results you achieve.

Why should you not view interviews as a test?

True or false: You shouldn't take any breaks when studying for interviews, because the relentless focus will help you learn the material better.

- *The more you care about your interview, the less likely you are to pass it.*
- Interviews are all about maintaining a nimble, flexible state of mind, so you can react gracefully when hit with a problem you haven't prepared for. This will almost always happen on interviews with tougher companies.
- If you view your interview as a "make or break" opportunity (which it admittedly very well may be) and go into it with a lot of anxiety, you will not have that healthy state of mind. When your interviewer goes off-script, your mental state will likely shatter.
- View interviews as a chance for you to go in and learn from some very smart people, and if you happen to get a job offer, that's a nice bonus. This much more relaxed and positive mentality will reduce your anxiety.

Related resources:

- [\[Taro Top 10\] Finding A Tech Job](https://www.jointaro.com/playlist/E6R0wVJ5EyCfVnVrlfno/)
- [\[Masterclass\] How To Ace Your Big Tech Interview - System Design](https://www.jointaro.com/lesson/J3I9fTW9ulr6T9KjxpqY/masterclass-how-to-ace-your-big-tech-interview-system-design/)

Struggling With DSA? Don't Worry, That's Healthy!

True or false: Interviewing is all about how much you know. As long as you study a lot and understand enough material, you will pass the interview.

For almost everyone, what does the learning curve look like for data structures and algorithms?

True or false: It's completely normal and even a good sign when you feel dumb while studying data structures and algorithms.

What's the most important piece when you're learning data structures and algorithms material?

True or false: If you aren't making progress on data structures and algorithms after 2 months, your mind probably just isn't geared to understand them. At that point, you should be practical and just look for interviews that don't have these kinds of problems.

Mastering data structures and algorithms (colloquially known as "DSA") is a frustrating experience: It requires such a different type of thinking and is rarely required on the actual software engineering job. This is why if you want to get good at DSA, you need the right mentality first.

Here are the core points from the video:
- DSA learning is an exponential process. You are going to be terrible initially, and eventually things will start to "click" once you hit the inflection point.
- When you feel dumb, understand that this is completely natural: This is almost a good thing as it's entirely expected.
- DSA is very much an input-driven exercise. Build a good process around studying it regularly and commit to it. With constant persistence, you will eventually get to where you want to be!

Related resources:
- [[Taro Top 10] Effective Interview Prep](https://www.jointaro.com/playlist/HLBYxJI7D1j8STolrwum/)
- [[Taro Top 10] Finding A Tech Job](https://www.jointaro.com/playlist/E6R0wVJ5EyCfVnVrlfno/)

One Diff, One Thesis - What Every Code Review Should Be Like

In this segment, we focus on the powerful practice of **“One Diff, One Thesis”**, along with a recap of three foundational principles for writing high-quality diffs. These habits dramatically improve your velocity, code quality, and perceived maturity as an engineer—especially early in your career.

### Key Takeaways:

- **We follow the “One Diff, One Thesis” principle**: Each code change should focus on a single, clearly defined purpose. Large, unfocused diffs (e.g., 5,000+ lines) dilute review quality and lead to blind approvals or unclear feedback.
- **We break big features into smaller, stackable diffs**: By submitting changes in 50–250 line chunks with logical dependencies, we keep discussions focused, reduce cognitive load for reviewers, and maintain a clear project history.
- **We pay the “20-minute tax” for high-quality diffs**: Investing time in writing strong context, test plans, and clear, focused diffs pays off by dramatically speeding up approvals and reducing iteration cycles.
- **We see these habits as promotion-worthy**: The most common reason junior engineers struggle during performance reviews is neglecting these basics. Conversely, those who consistently apply them stand out and accelerate their growth.
- **We optimize for long-term velocity and trust**: Thoughtful, well-structured diffs help reviewers engage more quickly and confidently, enabling faster delivery and demonstrating leadership-level attention to detail.

These three practices—**providing strong context, writing solid test plans, and submitting focused diffs**—are low-effort, high-return habits that every engineer, especially early in their career, should master.

What A Test Plan Is And Why Every Code Review Needs One

In this video, we explore the value of including a **clear test plan or demo** with every code review. This simple but often overlooked practice builds trust, improves collaboration, and turns code reviews into lasting pieces of product and engineering history.

### Key Takeaways:

- **We always include a test plan or demo**: Whether it's a video, GIF, image, or command-line instruction, we attach an artifact that shows what our code does—especially when it impacts visual or user-facing components.
- **We demonstrate the *actual* behavior of our code**: A video showing the happy-path functionality proves the feature works and saves reviewers from guessing or manually testing our changes.
- **We create long-term product context**: Test plans turn code reviews into searchable, durable documentation. They help future engineers (including ourselves) understand *why* a piece of code was written and what it was supposed to do.
- **We reduce risk when extending legacy code**: When we need to modify or build on old code, a past test plan often reveals how that code works, what features it powers, and how to avoid breaking it.
- **We invest a few minutes now to save hours later**: Including a test plan takes only 5–10 minutes but dramatically improves team empathy, speeds up reviews, and enhances the maintainability of the entire codebase.

This practice is one of the easiest and most impactful ways we show care for our teammates—and for the long-term health of the code we ship.

The Most Common Mistake Made When Authoring Code Reviews

In this video, we focus on the **critical importance of providing context** when submitting code for review. As codebases grow in size and complexity, understanding the "why" behind a change becomes just as important as the code itself. Without clear context, reviewers are left in the dark—and teams lose valuable time and trust.

### Key Takeaways:

- **We explain the “why” behind every diff**: When submitting code, we clarify *why* the change exists, *why* we chose this approach, and *why* alternatives were rejected. This empowers reviewers to assess our choices without second-guessing or chasing us for clarification.
- **We include business, technical, and historical context**: Good diffs often explain the strategic purpose, relevant architecture, past experiments, and legacy code decisions—all of which help reviewers understand how our change fits into the larger system.
- **We reduce friction with thoughtful summaries**: A well-written diff summary is low-effort but high-impact. Taking 5–10 minutes to write it may feel like a tax, but it dramatically speeds up reviews and earns the gratitude of teammates.
- **We acknowledge that no one sees the whole system**: Using the “elephant” metaphor, we recognize that everyone sees only part of the codebase. Providing our view helps others see how our changes fit into the broader system, making collaboration more effective.
- **We write for humans, not just machines**: Code may be infinitely flexible, but humans need context to interpret it. By empathizing with reviewers and anticipating their questions, we make code review a faster, smoother, and more collaborative process.

The #1 Principle To Remember When Authoring A Code Review

In this video, we discuss the most important mindset shift for code authors: **writing code with empathy for reviewers**. Code review is a collaborative act, and how we prepare and present our diffs directly impacts team productivity, trust, and code quality.

### Key Takeaways:

- **We write diffs with empathy**: The most valuable thing we can do as authors is consider how to make the review experience smoother for others. Clear, concise diffs help teammates help us faster and more effectively.
- **We respect reviewers’ time**: Reviewers choose to help us—they’re not obligated. Reviewing interrupts their own deep work and requires mental context-switching, so we express appreciation by submitting clean, readable diffs.
- **We build trust and social capital through quality**: Writing thoughtful diffs with summaries and test plans helps us gain faster approvals and earns the trust of teammates and tech leads.
- **We know we’ll be reviewers too**: As we grow in our careers, we’ll spend more time reviewing others’ code. We want our teammates to be just as thoughtful when submitting their diffs to us.
- **We lead by example**: High-quality, empathetic diffs set the cultural tone for our team. We improve the entire engineering environment by holding ourselves to the same standards we hope to receive.

Code Review Is NOT For Catching Bugs

In this follow-up video, we dive deeper into the **true purpose of code review**, challenging the misconception that its main goal is to catch bugs. Instead, we emphasize how code review plays a central role in shaping team culture, accountability, and knowledge sharing.

### Key Takeaways:

- **We see code review as a cultural engine**: It’s a key mechanism for how our team communicates, transfers norms, and builds a shared understanding of quality and standards.
- **We’ve learned that code reviews aren’t about bug-hunting**: Studies—including from Microsoft Research—show that code reviews are not effective at finding bugs, even with multiple reviewers or approval layers.
- **We hold ourselves accountable for our code**: The responsibility for quality and testing lies with the author—not the reviewer. Code review isn’t a safety net for catching functionality issues.
- **We avoid over-reviewing**: Adding more layers of review slows us down without meaningfully reducing bugs. Instead, we focus on meaningful reviews that support learning and alignment.
- **We use code reviews for knowledge and culture transfer**: Through thoughtful feedback and shared context, we reinforce how we build, think, and collaborate—making code review a vital part of team growth.

Tech Career Growth Videos

Our instructional videos provide software engineers with insights needed for career growth. Masterclasses go incredibly in-depth on a specific tech topic while case studies are real examples from established engineering leaders.