Three months ago I went through the Walmart Global Tech data engineer interview for a senior DE role. Bentonville team, remote position. Here's the full breakdown.
First thing to know: Walmart runs a massive internal data platform. They've been investing heavily in their own data infrastructure since the Jet.com acquisition days. The DE interview reflects that: they care a lot about practical pipeline design, not just LeetCode.
Interview stages: Recruiter screen (30 min, background) Technical coding round (60 min, SQL + Python) System design / pipeline architecture round (60 min) Behavioral round (60 min, STAR-heavy)
Coding round. SQL first, then Python. SQL questions: complex aggregations on a transaction table, a question on deduplication using row_number() partitioned by customer + timestamp, and a query optimization question where they gave me a slow query and asked what indexes I'd add. Python: a data transformation task, given a nested JSON representing store inventory, flatten it into a tabular format efficiently. Not hard, but you have to know your Pandas and be comfortable explaining your choices.
Pipeline design round. "Design an end-to-end pipeline to ingest point-of-sale data from 4,600 stores in real-time, transform it, and make it available for downstream analytics and ML models."
I talked through: Kafka for event streaming at the edge, Spark Streaming for transformation, Delta Lake / Iceberg for the storage layer, Airflow for orchestration of batch jobs layered on top. They pushed on: how do you handle late-arriving data? What happens when a store's network goes down? How do you monitor pipeline SLAs?
Schema evolution came up explicitly. They asked how I'd handle a case where the POS system at one store starts sending a new field that the rest don't have yet. I talked through Avro schema registry and backward compatibility. Good signal.
Behavioral round. Standard stuff: a time you had to debug a production pipeline failure under time pressure, a time you influenced a decision you didn't have authority over, a time you simplified something that was unnecessarily complex.
Overall: this loop is more applied and less algorithmic than a Google or Meta DE loop. If you've worked on real production pipelines with messy data, that experience translates well. If you've only done LeetCode prep, you'll feel the gap in the system design round.
Timeline for me: OA to offer was 6 weeks total. Not fast, not terrible.