Nike Data Engineer Interview Guide

In a recent project, I collaborated with a diverse team of data engineers and analysts to develop a data pipeline for a retail analytics application. My role was to design the ETL processes. There was a disagreement about the best tools for the job, with some team members preferring Apache Airflow while others suggested a simpler solution. I facilitated a meeting where everyone could voice their opinions, and we collectively evaluated each option against project requirements. Ultimately, we chose Airflow for its scalability. This experience taught me the value of clear communication and compromise in a team setting.

I once encountered a significant data quality issue in a production data pipeline where corrupted data was causing failures in downstream analytics. I initiated a root cause analysis, which revealed that the problem stemmed from a data source update that had not been communicated to our team. I quickly collaborated with the data source team to resolve the issue and implemented checks to validate data before ingestion. This proactive approach not only solved the immediate problem but also established a new protocol for future updates, enhancing our data pipeline's reliability.

In my previous role, I was tasked with integrating a new cloud-based data warehouse solution, Snowflake, into our existing architecture. Despite having minimal experience with Snowflake, I dedicated time to online courses and documentation to build my knowledge. I also reached out to community forums for specific questions. Eventually, I successfully migrated our data pipelines to Snowflake, which improved query performance significantly. This experience underscored the importance of self-directed learning and utilizing community resources.