Tell me about a time when you had to keep a mixed team aligned under a tight deadline, especially when one senior engineer and one junior teammate wanted very different approaches to the same data pipeline issue.

S: In my previous role, we were on a 4‑week sprint to deliver a real‑time ETL pipeline for a new product launch. The senior engineer wanted to use a complex Kafka Streams topology, while the junior engineer preferred a simpler Spark Structured Streaming job. T: My goal was to deliver the pipeline on time while maintaining code quality. A: I called a short stand‑up, asked each to outline the pros and cons of their approach, and mapped them against our constraints: latency, resource usage, and team skill set. I then facilitated a quick prototype comparison, running both on a small dataset to measure latency and resource consumption. R: The results showed that Spark’s simpler approach met our latency target and was easier for the rest of the team to maintain. We adopted Spark, documented the decision, and the sprint finished on schedule with a robust pipeline. The senior engineer appreciated the data‑driven choice, and the junior engineer gained confidence in contributing to architectural decisions.