In the technical interview at Globant for a Senior Data Engineer role, they typically start by validating your English and communication skills, then move into a broad but practical technical discussion covering Python (generators, OOP, GIL, memory optimization, ETL patterns), SQL (joins, window functions, aggregations, ranking queries), Spark/PySpark (lazy evaluation, transformations vs actions, shuffle, repartition vs coalesce, optimization strategies), and cloud architecture—usually AWS-focused with questions around S3, Glue, EMR, IAM, KMS, and designing scalable data pipelines. Rather than pure algorithm-heavy coding, they often emphasize real-world problem solving, such as how you would process large datasets, optimize slow Spark jobs, design secure ETL systems, or troubleshoot production issues. You may also get live exercises like parsing JSON/CSV data, building transformations, or solving medium SQL challenges, but the biggest focus is usually on how deeply you understand performance, scalability, and business trade-offs, since they are often assessing whether you can work directly with enterprise clients and explain technical decisions clearly.
