Avanade Data Engineer Interview Guide

The interview process at Avanade for the Data Engineer role spanned four rounds over several weeks. It started with an HR screening call focused on general background and experience, then moved into three technical interviews. The technical rounds were heavy on their core stack—Azure, Databricks, and Spark—but the difficulty level was moderate if you had hands-on experience with those tools. I got questions about PySpark and Spark SQL, including things like identifying duplicates in datasets using window functions or groupBy operations. One interviewer also asked about DBT and Microsoft Fabric, probing whether I'd worked with modern data stack tools. The technical questions weren't algorithmic puzzles; they were practical scenarios anchored in real-world data engineering work. The interviewers were genuinely helpful and collaborative, which made the technical rounds feel more like a conversation than a grilling. One thing that caught me off guard was a behavioral question about working beyond scheduled hours without formally recording overtime—it felt like a test of willingness to go above and beyond, but the phrasing made me uncomfortable. Despite making it through all four rounds and feeling like the technical discussions went well, I didn't receive an offer. It's hard to pinpoint exactly why; the feedback was minimal. My sense is they were looking for someone with deeper, more production-level experience in their specific stack, or perhaps the cultural fit assessment during the soft rounds didn't align with what they needed.