I Scored 453 Data Engineering Stack Overflow Questions for Readability — Here's What I Found

I analyze a lot of text in data pipelines. Document ingestion, user feedback processing, content quality checks — anything where you're batching text from an external source and need to know if it's usable. One thing I've never done is systematically measure what "good" looks like. So I picked Stack Overflow as a test corpus: thousands of real technical questions, with upvotes as a quality signal. If higher-voted questions are written more clearly, that would be evidence that readability scores have real signal value in a pipeline. Here's what I found. The Setup I pulled questions from Stack Overflow's public API across five data engineering tags: data-engineering , apache-spark , apache-airflow , dbt , and apache-kafka . I used the most-voted questions for each — no auth required, just the public API. After deduplication: 453 questions , each scored with three readability metrics: Flesch-Kincaid Grade Level — maps reading difficulty to US school grade (grade 8 = readable by most adult