I applied for a Data Engineer role at Intuit and went through their interview process starting with an online assessment. The OA consisted of four medium-level questions covering SQL, bash scripting, and a coding problem. What caught me off guard was the language restriction — they only allowed Java or Python, so I had to commit to one early on. I chose Python and moved forward.
After passing the OA, I had my first live interview focused on CS fundamentals and data structures. This was where they dug into algorithmic thinking. One question involved working with a monotonic stack, which tested my ability to optimize solutions beyond the brute force approach. Another asked me to write a SQL query to find the second highest GPA in a dataset — straightforward on the surface, but the interviewer was clearly checking whether I understood edge cases like handling ties or NULL values.
The final round was a comprehensive deep dive with the hiring team. They reviewed my resume in detail, asked behavioral questions to assess communication and fit, and circled back to more complex DSA problems. This round felt like they were testing not just technical chops but also how I think through problems, ask clarifying questions, and handle feedback. The interviewers were direct but fair, though I left feeling like I'd stumbled on a couple of the harder algorithmic variations they threw at me.
I didn't receive an offer, which in hindsight I think came down to not being sharp enough on the DSA problems under time pressure. The process itself was well-structured — three rounds, clear progression from medium difficulty to comprehensive assessment — but I underestimated how much they'd push on optimization during the final round.
Prep tip from this candidate
The monotonic stack problem and SQL edge cases like handling duplicate highest values were key focus areas — practice these specific patterns under timed conditions, and be extra careful with SQL NULL handling and aggregation edge cases since they seem to test those deliberately.
