Spin Data Analyst Interview Guide

When faced with a slow SQL query, I would start by analyzing the execution plan to identify bottlenecks. This involves looking for full table scans, missing indexes, or inefficient joins. Next, I would consider rewriting the query to use subqueries or common table expressions (CTEs) for better readability and performance. Additionally, I would evaluate the indexing strategy on the tables involved, ensuring that the most frequently queried columns are indexed. Finally, I would test the query with different datasets to measure performance improvements and iteratively refine my approach based on the results.

To analyze the drop in daily active users, I would first segment the data to understand user behavior over time, looking for patterns or anomalies. I would employ cohort analysis to compare user engagement across different timeframes. Additionally, I would examine any changes in product features, marketing campaigns, or external factors (like seasonality) that may have contributed to the decline. Using statistical methods, I would conduct A/B testing if applicable, and correlate the findings with user feedback to gain insights into potential issues.

In a previous analysis, I discovered discrepancies in sales data between two sources. I first validated the data by cross-referencing it with a third source and conducted exploratory data analysis to pinpoint the inconsistencies. I used SQL to write queries that highlighted mismatches, and then I collaborated with the data engineering team to identify the root cause, which was a data ingestion error. After resolving the issue, I implemented data validation checks to prevent future discrepancies, emphasizing the importance of data quality.