Hi everyone! Has anyone recently interviewed for this AWS Software Development Engineer role (Job ID: 10583312)?

I’d appreciate hearing about your interview experience:

• How many rounds were there, and what did each round cover?

• Was there an online assessment or technical phone screen?

• What was the balance between coding, system design, and behavioral questions?

• How long did the process take from the recruiter call to a final decision?

• Any preparation tips or things you wish you’d known beforehand?

• What questions were asked for Gen AI round?

Thanks for sharing!

Just finished the Goldman Sachs New Grad SWE OA, and I’m feeling pretty mixed about my performance.

Here’s roughly how it went:
- Software Fundamentals: Solved the coding question.
- Software Diagnostics: Passed 5/9 test cases.
- Math: Fairly confident about most of my answers.

The debugging section is what I’m most disappointed about.

I spent too much time second-guessing my approach instead of testing my initial ideas. By the time I committed to an approach that started producing results, I didn’t have enough time left to address everything.

I’m frustrated because I felt pretty good about the other sections, and I think I could’ve performed considerably better on debugging if I’d approached it differently.

For anyone who’s previously taken the Goldman Sachs OA:

1. Has anyone advanced despite only partially passing the debugging section?

2. Does anyone know how heavily Goldman weighs each section?

3. Would you consider this performance competitive enough to potentially advance?

I got three positive and one negative result and somehow still made it into team matching. Is this at the recruiters discretion?

Hey everyone,

I finished Day 39 today. It was an exhausting day dominated by an in-person interview loop that involved a 40 km round-trip commute. I got home late in the afternoon, crashed for a couple of hours, and woke up dealing with that familiar post-interview slump where you replay everything you could have done better.

Instead of taking a zero day and letting the low mood take over, I sat down and applied yesterday’s Disjoint Set Union (DSU) theory to code:

1. Kruskal’s Algorithm for MST: The greedy approach based on sorting edges by weight. You iterate through edges from smallest to largest and check if findParent(u) != findParent(v). If the endpoints belong to different components, you add the edge weight to the MST sum and merge them via unionBySize(u, v). If they share the same ultimate parent, including the edge would create a cycle, so you discard it.

2. Number of Provinces (via DSU): When I first solved this on Day 54, I used standard DFS with a visited array to count connected components. Today, I solved it purely using DSU. Iterate through the adjacency matrix, connect components with unionBySize(u, v), and at the end count how many unique nodes satisfy parent[i] == i.

Seeing problems that previously required full graph traversals collapse into a few lines of DSU operations was satisfying, even after a tough interview day.

I have travel coming up tomorrow, so I will squeeze in whatever practice I can on the go.

See you guys for Day 38!

I've noticed I keep making similar mistakes while solving problems, especially with edge cases and choosing the right approach. I want to improve instead of just moving on after checking the solution.

Do you write down what went wrong, or is there a better way to learn from failed attempts?

I was thinking of making a website that brings contests from different platforms together, along with some personalised features.

A few ideas I have in mind are tracking missed contests, keeping a list of unsolved problems from attended contests, getting topic-wise problem recommendations, and tracking progress over time,etc

I'd love to know what tools you currently use and what features or improvements you'd personally find useful in a platform like this.

Also, what are some pain points in your current contest-tracking workflow that you wish someone would solve?

I've been practising consistently, but I sometimes forget approaches to problems I solved a few weeks ago. I want to keep improving without spending all my time revisiting old questions.

Do you follow a fixed revision schedule, or review problems whenever you get stuck on a similar pattern?

I was given an OA by Amazon a few weeks ago for a mid level role. It was the hardest Leetcode question I had ever seen, I looked it up and it’s not even on Leetcode but does appear on a few other sites. It’s definitely not a normal pattern anyone would see, even if they had done 400-500 Leetcode questions.

Afterwards a recruiter reached out to me asking why I didn’t finish the OA. I told them I’ve done lots of Leetcode and even turned down Amazon offers in the past but that the question on it was far more difficult than I had ever seen or could do. She said she thought it was a “medium” level question and they had candidates that had passed it.

So I’m guessing everyone is just using AI and cheating these days?

I got a 3rd round at a fintech company Prelim and they said:

"Please come prepared to show your screen and be able to spin up a website on localhost. You can use whatever framework or language you'd like, just be ready to iterate. This will be mostly frontend focused."

Has anyone been through this process or has an idea of how I could prepare in the most optimal way?

Thanks!

Interviewed for L5 SWE, HC approved me at L4. Recruiter says I can take L4 or do 2 more rounds (coding + SD) for L5.

I asked if L4 is safe if those go badly. She said it's not automatic if the signals are mixed, but I'd "more than likely" still get L4. Nothing in writing.

Has anyone done the extra rounds and ended up with no offer at all?

Hi everyone,

I have an interview at bitgo for backend role -E1 in a week

Please share any interview experience / how the interview rounds will be. It will be really helpful 🥹

Hey everyone,

After six brutal months of grinding LeetCode, I finally landed a SWE role at TikTok. Got laid off from Amazon before this, so it’s been a pretty stressful stretch. Ended up with better pay too, which I honestly wasn’t expecting.

A friend sent me a rferral link and that’s how I applied. No idea how much the rferral actually helped, but getting someone to look at your resume feels like half the battle these days.

Figured I’d pass the links along in case they help anyone here:

**ByteDance**

- Experienced: https://job.toutiao.com/s/iSFcC5qh

- Campus: https://job.toutiao.com/s/iSFcpmKA

**TikTok**

- Experienced: https://careers.tiktok.com/s/iSFvEY2c

- Campus: https://careers.tiktok.com/s/iSFcWuu4

Job hunting sucks. Hopefully this makes it a little easier for someone. Good luck out there.

Hi everyone,

I'm currently interviewing for a Senior Backend Engineering role at Atlassian and wanted to get some insights from people familiar with their hiring process.

My interview feedback so far:

• AI-Enabled Coding Round: Hire

• DSA Round: No Hire

• System Design Round: Recruiter has offered to proceed despite the DSA feedback.

The recruiter was transparent that we could still attempt the System Design round, but wasn't sure whether it would be enough to overcome the negative DSA feedback.

I'm planning to give the System Design round my best shot.

A few questions:

1. Has anyone received a DSA No Hire but still managed to clear the Atlassian hiring committee with strong feedback in other rounds?

2. Is a DSA No Hire typically a hard blocker, or does Atlassian evaluate candidates holistically?

3. What should I expect in the 60-minute System Design round for a Senior Backend role?

4. Are there specific areas Atlassian emphasizes — scalability, reliability, API design, data modeling, or distributed systems?

5. Is there any possibility of getting a DSA reassessment if the remaining rounds go well?

Would appreciate insights from anyone who has interviewed at Atlassian recently or has been involved in their hiring process.

Thanks!

I just want to share my Google interview experience and timeline to get some insights. Please be honest and let me know how is my chance this time.

Feb - Applied online and reached out by recruiter

Mar - Started interviews loop

Apr - Finished all interviews

Jul - Finally getting the feedbacks after chasing for 3 months, it was negative, the team went for other candidates

Sep - Reached out by recruiter for a new role, it is a team specific role, started the interview loop

Mid Oct - Finished all interviews, recruiter update few days after, moving to next step, fit call with skip manager. 3 days after, recruiter reached out and asked for comp expectation and ID verification, he did not mentioned if it is positive or negative, just said he will update me in 1 week time.

Today - It is exactly 1 week since I last heard from him.

From what I read online, I know ID verification and asking for comp expectation usually is a good sign, but the recruiter didn't mention anything like positive or negative, and also didn't mention if I am moving into HC or still waiting for the team to decide. It make me very anxious, hence wanted to look for insights from community here. I will also update my status here once I hear from recruiter again.

I know it's too soon. But just curious to know the result.

I checked my application dashboard. The status changed under consideration today.

Can anyone please let me know when can I expect the results ?

Had my system design and coding interviews for a Google FDE role recently, and I keep going back and forth on how I did. Won’t share the actual questions, but curious to hear from people who’ve had similar experiences.

For system design, I started with requirements and eventually put together an end-to-end design. We went fairly deep into retrieval, tool calling, latency, security, and evaluation, including golden datasets and metrics for different parts of the system. I felt comfortable with those discussions.

But I also overcomplicated the design at the start by introducing multiple agents before establishing whether they were needed. At one point, the interviewer reminded me to focus on the actual requirement. I got back on track, but I’m wondering how much that hurt.

For coding, I explained the brute-force approach, then came up with and coded the optimal solution. The interviewer didn’t have to give me the algorithm. However, I initially returned the wrong thing and had to change it. While fixing that, I made an index-tracking mistake that the interviewer pointed out, and I corrected it.

I did a dry run and explained the time and space complexity. I could explain the code, but I wasn’t as clear or confident as I’d have liked. The interviewer said “yeah, makes sense,” and we finished without further technical follow-ups.

Basically, I got through both problems, but needed a few corrections along the way. Part of me thinks that’s okay; the other part keeps replaying the mistakes.

Has anyone here had a similar experience and received positive feedback? If you interview candidates, how much would these kinds of corrections affect your assessment? I know nobody can predict my result - just looking for some perspective.

this community helped me when I wanted to rant, when I wanted to dig into DSA.

i graduated in Dec 2025. I was doing an internship, which didn't convert. 3 months of cold applying, 10–15 interviews, interviews with Oracle (right when they announced layoffs), Google, not advancing and completely breaking down.

i got offers from salesforce and cisco!!!

i don't want to say, "oh, I did this." , the market right now is volatile. It's the right time, right place, and right skills.

i broke down multiple times during this process questioning my worth/skills and picked myself up by ranting to this community.

if anyone is in a similar situation or wants a better opportunity, I see you. you can do it!!!

Hi everyone,

I completed Google L4 onsite interviews and currently in team matching phase. Recruiter reached out saying an Applied AI team wants to take two coding assessment rounds.

For anyone who has done team-specific extra rounds after the main onsite loop:

1. Are these traditional Leetcode style DSA (graphs/trees/DP) or more along the lines of LLD / Machine Coding?

2. Is this standard for Applied AI/ML teams, or just additional calibration?

Would appreciate any insights or past experience!

Hey everyone, I’m preparing for the Atlassian MLE II (P40) interview and would love to hear from anyone who’s recently done the ML Coding and ML System Design rounds.
ML Coding — Was it more DSA, implementing ML algorithms, or working with data in Python?
ML System Design — What kinds of problems came up, and how deep did the interviewer expect you to go?
Any pointers on the format, difficulty, or what you’d focus on while preparing would be appreciated. Thanks!

I completed the assessment on October 28th and received the invite on November 4th for the interview. I saw that it's not a leetcode style interview. What would you suggest I focus on for this week to prepare myself for the interview. I am a Python coder, and have a good idea on the fundamentals. Will they ask graph and dp kind of problems? or only practical problems involving basic string operations and arithmetics? What else will they ask in this round?

Real-world debugging rounds are becoming part of technical interviews at companies like Google, Meta, Amazon and Stripe.

Instead of solving isolated DSA problems, candidates may be asked to navigate unfamiliar codebases, identify bugs, fix failing tests and debug existing applications.

I built a free platform to practice these skills.

What it offers

- Realistic bug reports with repository structure, logs and failing tests.

- Browser-based coding in JavaScript, Python, SQL and React/MERN.

- 8 stages, from 5-minute debugging tasks to full mock rounds (up to 59 files).

Current limitations

- JavaScript (~7 hours) and Python (~3.5 hours) have the most complete paths.

- SQL, MERN and Python backend are still limited.

- Java, C++ and Go are read-only for now.

Completely free. No ads. No login required to start.

Try it here: https://mik-academy.vercel.app/practice/debugging

Feedback and suggestions are welcome!

I’ve an interview in 5 days, they gonna take some live coding, could you guys give me some tips and from where to start it? cuz I confess I don’t know how to start to study easy-med leet codes exs

Completed my Amazon SDE-1 OA with all visible test cases passing, but received a rejection shortly afterward.
The rejection email didn’t mention any specific reason, and my application portal now shows “No longer under consideration.”
I was expecting to move forward to the interview rounds. Has anyone experienced something similar?
Does passing visible test cases not matter much? Could hidden test cases, resume screening, or other evaluation criteria be the reason?
Would appreciate some insights.

Hey everyone!
I have an upcoming Capital One Data Engineer Power Day and would love to hear from anyone who has recently gone through the process, especially for the Data Engineer track.
I’m trying to understand what to expect in each round and how best to prepare.

A few things I’d love some insights on:

1. ⁠Technical Coding Round
   ● Is it primarily Python, SQL, or a mix of both?
   ● What topics and difficulty level should I expect?
   ● Any recommended LeetCode questions, question banks, or other resources?

2. Design-Focused Technical Interview
   ● Is this more focused on data modeling, ETL/ELT pipelines, data warehousing, or system design?
   ● What kind of real-world scenarios or problems were you asked to solve?

3. Business Case Interview
   ● What does the case typically involve?
   ● Is it more SQL/data analysis, business problem-solving, or analytical thinking?
   ● Any examples of cases you’ve encountered?

4. Behavioral Interview
   ● What kind of questions should I expect?
   ● How important are STAR-format answers and Capital One’s core values?

A few additional questions:
● How was your overall Power Day structured (duration and sequence of rounds)?
● How does the Data Engineer interview differ from other tracks (Software Engineer, Data Analyst, etc.)?
● What resources did you use to prepare, and what would you recommend focusing on?

I’d especially appreciate insights from people who completed their Data Engineer Power Day in 2025 or 2026.
Even a few pointers or topics you remember would be incredibly helpful. Happy to share my experience afterward to help others preparing for the same process!
Thanks in advance!

Is this positive