Learning Quickly Q&A and Videos

One thing I have noticed about competent engineers is that they have a very strong understanding of upstream and downstream dependencies in terms of what are the typical parameters being passed into our API's and what we return and what are the things we pass into our dependencies API's and what they return,

How can I learn the context behind what data is typically being passed quickly? I understand that one of the best ways to learn is to start tinkering, and call the API's myself and see what they return. I was wondering if there is any faster way because surely a staff/principal/TL doesn't have the time to tinker with all of the different dependency services and understand what they take in and return. Is it just a matter of reading documentation and asking around?

As part of my story or task, often times I have to understand and contribute to other codebases or solve bugs that involve understanding flow of other codebases. What is the best way to navigate this when you are suppose to solve an issue where you have to understand an external codebase in order to contribute or fix an issue?

I’ve watched the Lego Model Video and I was wondering: What are current most high demand legos / lego sets/ skills in your opinion?

Joining a FAANG (Amazon) as a senior eng after being mid level at another FAANG. The team uses Go programming language which I've never used before. Should I work hard on learning this language (in the week I have before starting) or just relax and wait till I actually start?

I'm looking for a good resources I can study from to strengthen my front end knowledge in react and type script. And may be built some projects to solidify the learnings. Would greatly appreciate any feedback/suggesstions.

Does working in different area - infra, developer experience, product, backend, frontend over the career period hurt in terms of becoming/staying a staff engineer or manager?

Greetings, everyone,

For the past five years, I've been working as a cloud engineer, mainly focusing on major cloud providers. My tasks have revolved around provisioning instances, configuring logs, and handling various operational aspects. However, I must admit that my job's scope is quite limited, and I find it challenging to discover more engaging opportunities. Unfortunately, the team responsible for automation work, although I occasionally collaborate with them, isn't the most receptive or cooperative bunch.

In light of this situation, I've decided to explore my interests further and pursue personal projects that align with my passion for Data Engineering. I've already started working on some of these projects and find the field incredibly fascinating.

While I'm aware that the current job market is unfavorable, I am not actively seeking new opportunities at the moment. Instead, my focus is on building a robust portfolio over the next year or two, with the intention of eventually pursuing a career in Data Engineering.

I believe that having one or two years of experience through personal projects in Data Engineering will improve my chances of securing interviews, as finding data-related opportunities within my current company has proven to be a challenge.

I'd appreciate hearing your thoughts on this matter!

I’ve seen questions recently about people wondering whether to pursue an MBA of a CS Masters which comes at the expense of either maintaining your current tech job or searching for a new one. My question is slightly different: When is it worth taking a learning sabbatical if ever?

By learning sabbatical, I mean I have seen people put on LinkedIn that they did quit their jobs to do a bootcamp in some field (Mobile, Blockchain, etc) or even just self-study on their own. I have a coworker who’s a business analyst and told me he’s quitting to do a data science bootcamp. Doesn’t seem like a good idea to me, but not my decision.

I think the general rule people follow is to have a job while looking for one, particularly in this economy. Still, I’m wondering about what circumstances actually warrant quitting a job to invest in getting one in a different field. Obviously being in a toxic environment is the best reason to get out of a current job. Similarly, if you really need the money, you probably can’t leave the job.

So let’s assume that neither is the case. You don’t need the money and the environment is positive, but you’re really not doing what you’re passionate about and feel like every day you are missing learning cool stuff. Say you’re a business analyst and want to become a data scientist or backend software engineer. An obvious move is to try and switch into these roles within your current company. But if you can’t do that, how to think about taking a learning break?

Hi Taro Community,

I’m a recent addition to my company and have embarked on my journey with a few initial tickets, completing 3 PRs in the span of 2.5 weeks. Alongside these tasks, I’ve been provided with architectural and design documents to help me grasp the broader system. However, these documents are quite high-level and, at times, challenging to comprehend fully, especially without knowing which sections are most pertinent to my current work.

Given the breadth of information and my eagerness to effectively contribute, I’m contemplating the best approach to balance my tasks with learning. Here are a few points I’m considering and would love your insights on:

1. Targeted Learning from Documents: Should I consult with my mentor on which specific parts of the document would be most beneficial for me to focus on initially, considering my current assignments?
2. Codebase Familiarization: How should I approach diving into the codebase? Is it advisable to ask my mentor for guidance on specific modules or areas that align with my tickets or overall team objectives?
3. Balancing Tasks and Learning: What strategies have you found effective for simultaneously working on assigned tickets, understanding high-level documentation, and becoming familiar with the codebase? Is there a recommended balance or sequence that could optimize my ramp-up process?

Navigating the initial phase and ensuring I’m building a solid foundation is my top priority. Any experiences, strategies, or advice you can share on managing these early stages of onboarding would be greatly appreciated.

Thank you for your support and guidance.

As I watch , I see the slide where it mentioned MVP and just 1 Thing. And also, in the slide, the example of the app is . I have couple questions about this.

1. To come up with the idea of Just 1 Thing application, I believe most of the ideas are already executed before. Is that something I do have to afraid of or not? When I search on app store now, I see similar apps to Random Name Picker. And I'm afraid for how we grow the number of users as most of users choose to use something existing with top rated.
2. If we take number 1 question as a factor (most of users choose to use something existing), how to come up with the idea about something that someone has not executed before?

I want to build on side projects by myself but to come up with something specific with quality. I'm thinking about to do system design doc (define specific requirement, breaking the big task to small task (make it granular, etc)). Try to make a plan before make any execution.

Please let me know if this plan works for me or not. I want to execute on how I can serve something to users that we all think it's useful.

I'm in a dilemma of wanting to change my career from a Data Engineering background to a SWE one.

My dilemma is about me not having too much of a coding background (too much SQL) and not wanting to do badly, alongside with probably having to accept less pay.

I'm a Data Engineer. Within the data engineering realm, there are a lot of tools, just like in the software engineering realm. The modern data stack is pretty popular these days. It includes things like Spark for ETL at scale, Docker for virtualized environments, Airflow for orchestration, dbt (data build tool) for transformations in SQL, Fivetran for automated data connectors, Snowflake for data warehousing, and more.

I'm far from knowing all of these tools well, primarily because I use very few of them in my day job. The main reason I want to change jobs is because of this.

I'm worried I'm caught in a catch-22 situation where I don't know the tools so I can't get jobs that have them, which I guess is similar to the new-grad cold start problem.

My question is, how should I think about learning new tools for job interviews? My current instinct is to learn via failure. That is, I have almost all of the above tools on my resume. If someone asks me about them and I'm not able to give a good answer, I will learn that part about the tool so if I'm in the same situation I can answer properly.

Another approach I can think of is to do Udemy courses of them so I have a deeper understanding of how they work. I've learned to be wary of course not tied to projects, though, so I'm hesitant.

I guess I could do projects to learn more about them, but those take time and right now I'm focused on applying to jobs.

I imagine some answers might focus on what my current problem is: can I get interviews or am I failing interviews? I don't think my issue is with failing interviews right now, and certainly not because of specific knowledge people have called me out for for not knowing these tools. I think my issue is more with sourcing interviews currently.

If there's general advice regarding how to think about prepping for an interview when you only have some of the requirements on the Job Description, would love to hear that too.

Thanks!

I often see advice online / receive feedback from so many different sources. That could be during manager 1:1, peer feedback, reading content on LinkedIn, etc. While I resonate with it, I often find it hard to track all the feedback / advice I receive and holding myself accountable to act on it or change my behavior. This is especially hard when the advice is not immediately actionable (i.e. situation based).

So my question is, how can I consolidate the learnings from all of these sources and make sure I act on it (or at the very least, remember it when a situation arises)?

What do you think about certifications in general? And specifically, what do you think about certifications like these

I was watching the course on "" video from the coding course

As an MLE how narrow should my focus of a tech stack be? and what constitutes as a tech stack?

99% of my work is in Python, but at 1 job I did 90% of my work in PyTorch

At my current job I'm doing 40% sklearn/keras (it's a simpler version of PyTorch implementing same things, but is mostly used for ML stuff where models need low customization) + Pandas

Rest 60% of my job needed me to start working with Polars which is pandas but faster and is more heavy weight (similar to PySpark/Dask)

Is that too much branching? or Should I be narrowing my focus?

TL;DR it's ideal to hone in on 1 tech stack, but changing circumstances need me to use different frameworks depending on the problem. Is that bad?

I have realized that I need a very high level overview of the current system and what other systems it communicates with. I have no idea on how to do it; I'm currently planning to have a meeting with my EM.

I have access to the Quip, Google Drive, and some brown bag sessions. I have watched a few of them, but it all seems very fragmented. After I figure all this out, I plan to create a doc which can help others better in the future.

After finishing my Bachelor's Degree in Computer Science and and working as a Machine Learning Engineer for ~5 years I'm looking for something new. So far, I've been mostly focused on building software for real time systems, deployments, basic modelling, information retrieval (search) and troubleshooting "legacy" code and systems.

I'm now preparing for a new round of interviewing and I hope to make the jump to a highly paid job, possibly in the US (I'm based in Europe).

After assessing my skills and profile, I noticed that my data engineering skills are really superficial and I'm absolutely not confident in this area. E.g. I know basic SQL, but haven't build a data pipeline or ETL, I don't know hadoop etc.

Now, my question is if it would be wise to do Zach Wilson's  Boot Camp.

What I'd hope to gain from it: data engineering skills, more confidence in this area, and a higher chance of succeeding in (finding) a high paying job.

Do you think it's worth the investment, both in terms of time and money? Or do you think I should direct my focus elsewhere?

I have worked primarily on the backend, but my company now has assigned me features concerning our Android app, which is built mainly on XML for layouts & Java.

How can I get started on this, be productive & contribute effectively?
Thanks.

I've only ever coded in js/ts; however, as I seek to grow my skills in react-native I've been trying to learn swift to deal with bridging. However, after coding a few hours in swift when I swapped back its like my brain stopped working and simple stuff I couldn't remember the syntax. After working in it again it kinda went back to normal; but, is this something that is normal or goes away with practice?

I worked 3.5 years on the backend for a large applications with the stack of java, spring, hibernate and mysql + bunch of internal frameworks. Now I switched to another company and for 3 years I worked on the backend for another large applications (thousands of users) but with multiple languages. There were microservices in python, go, java and C++. I was also required to write some react code. While I was able to hack around (not the top notch code) and ship things that work, I was wondering if this kind of experience will damage my profile in the job market, since everyone wants someone that is specialized.

I pretty much interact with everyone else through text-based means, which is all really pronounced in this remote covid era. However, I like to learn from other people directly, talking with them. For example, I'm currently working with a senior full-stack engineer doing things like pair programming and that has been helpful.

What can I do to get more of this deeper learning instead of trying to learn almost entirely through text?

I am moving teams for the first time in my three-year long career and the team I am moving to is full of very experienced folks(15-20) with the least experienced person with 10 YoE. Some of these folks have also been in the same team for years. I will be new in the team as well as tech stack.

How do I create impact, learn from these people, be able to create my own identity and not be shrugged off as a "junior engineer" who is not given important tasks?

Any additional tips on how can I leverage this move as a fresh start? Like relationship with manager, team and my attitude towards the work?

I am moving to a different organization within my current company where I will be working with a new domain and new language (C#)

Looking for some concrete advice to get clarity in terms of expectations from my new manager and also work with him to upskill myself in the new product fast.

I am also looking for concrete tips on how to upskill beyond syntactical on more intermediate and advanced concepts in the new language? How do other engineers approach learning a new language beyond the surface.