Just finished the Cohere data engineering loop in late May 2026. Going to dump what I remember because I couldn't find anything detailed when I was prepping.
The process was four rounds total after the initial recruiter screen. No take-home, which I appreciated.
Round 1: Technical phone screen (45 min) This was SQL-heavy. The interviewer shared a schema in the browser and walked through three progressively harder queries. Think window functions, CTEs, some aggregation with filtering on large-ish tables. Not trick questions, just steady SQL work. One question involved identifying data quality issues in a sample pipeline output, which I thought was a nice change from pure syntax testing.
Round 2: System design for data (60 min) Design a pipeline to ingest and serve model inference logs at scale. They care about latency vs. throughput tradeoffs, schema evolution (their models change constantly so your pipeline has to not break), and idempotency. I talked through Kafka for ingestion, Flink for streaming transforms, Iceberg for the storage layer. They pushed back on Iceberg specifically, wanted to hear my reasoning vs. Delta Lake. Not gotcha, just genuine engineering discussion.
Round 3: Coding (45 min) Python-focused. One question about writing a transformation function on nested JSON (realistic for ML metadata). One about debugging a broken Airflow DAG from a screenshot. Not LeetCode-style at all. More like real day-to-day data eng work.
Round 4: Behavioral + cross-functional Standard impact questions. A few on how I'd work with ML engineers to define data contracts. They seem to actually care about cross-team communication which makes sense given their model development culture.
Total time from first contact to offer: about 5 weeks. Offer came three days after the final round debrief, which was a nice turnaround.
The interviewers were sharp and the SQL stuff was genuinely the most interesting technical screen I've done in a while. If you're a data eng with an ML/AI company background you'll fit right in. If you're purely warehouse/BI-focused, I'd brush up on streaming concepts before going in.