Overall: Positive Experience | Did Not Receive Offer I didn't end up getting the job, but I had a genuinely positive experience throughout the process and wanted to share what I learned to help others prepare. Round 1 – HackerRank Test (Resume Screening) You get this if your resume is shortlisted. It had 2 SQL questions, medium to hard difficulty. They're doable if you manage your time well. After completing this, I emailed the recruiter expressing my enthusiasm and confidence in moving forward, and also sent along my updated resume. Round 2 – Recruiter Screen The recruiter was warm, cheerful, and straight to the point. She asked me about the project I'm most proud of and whether I had experience using LLMs/AI in real work projects (not academic). She also covered salary expectations, relocation, and the usual screening topics. Afterward, she set up a separate call to walk me through the full interview process, what to expect, and the next steps. Very helpful. Round 3 – HackerRank Coding Round (Live, Back-to-Back with Round 4) This had 4 questions. The first was SQL — not super hard conceptually, but the question was a bit cryptic. I couldn't clearly see the input and output tables, which made me panic a little. If you're taking this test, don't panic. Read slowly and take a moment to absorb all the information before jumping in. The next 3 questions were Pandas/PySpark-based — dataframe manipulation and feature engineering. I had experience with both, so I prepared both, but that turned out to be a bad decision. I got confused switching between syntaxes, which slowed me down. I pulled myself together and solved questions 2 and 3 but couldn't complete question 4. A few things to note: they give you the option to Google things, but you need to share your screen so the interviewer can see what you're searching. My interviewer was giving hints and was genuinely helpful. I completed 2 out of 4 questions by the end. Round 4 – PEI + Problem Solving (Case Study) This round started with a PEI (Personal Experience Interview) question in the Connection area: "Explain a challenging situation you encountered when working with someone who had an opposing opinion." From what I've seen, Data Engineer roles tend to get either Connection or Leadership PEI questions, but prepare all four areas well (Growth, Drive, Leadership, Connection). I had my story well prepared, so I was able to deliver it clearly and answer all the follow-up questions. Then came the problem solving / case study. This was the scariest part for me — it was my first consulting interview and I had never even heard of case studies before seeing the interview process. But to my surprise, it wasn't purely business-oriented. It was tech-based, though you do need to know basics like revenue, margin, CAC, break-even, etc. The questions were about revenue, margin, and profits but applied to a technical problem. The scenario involved a company with siloed platforms, no centralization, mixed internal and external data sources. I had to identify problem areas, answer quantitative questions about revenue increases from upgrading the system, and determine whether the upgrade would be cost-beneficial. The interviewer was patient, answered all my clarifying questions, and I even got positive feedback from her at the end. Rounds 5 & 6 – Final Rounds (I Did Not Reach This Stage) If I had moved forward, there would have been 2 more rounds: another case study / problem solving round, a behavioral interview, and I believe a resume walkthrough. These final rounds are conducted in-person at the office. Result I was rejected after Round 4, and I kind of knew it would come down to the technical round. When I asked for feedback, they confirmed I needed to work more on technical skills. The result came quickly, so I wasn't left waiting around, which I appreciated. Overall Thoughts The experience was great. They provided enough preparation material, the recruiter was kind and helpful throughout, and I felt valued during the entire process. Everyone I interacted with was supportive and willing to help when I got stuck. Obviously I was sad — it was a great opportunity — but I learned a lot from the process and I know exactly what mistakes not to repeat. My Tips for Future Candidates: Pick either Pandas or PySpark and prepare it thoroughly. Don't try to juggle both — the syntax confusion will slow you down. Practice SQL without relying on visible input/output tables. Be proactive — you might be given a dataframe and told "these are the input datasets and this is the query," then asked to identify what's wrong or join with another table. Read slowly, don't panic. Make sure you practice declaring dataframes, loading from CSV, and converting nested JSON to dataframes. Prepare your PEI stories really well. Use the interview prep materials McKinsey provides — they're genuinely useful. For the case study, don't panic. You won't get a purely business case — it'll be a tech problem with business context. Practice from the materials they give you. I hope I get another chance to interview with McKinsey — generally the cooling period is about 1 year before reapplying to another role. This time I won't repeat my mistakes. All the best to everyone reading this. Please prepare well — it's a great company. I hope you get it!