What problem does it solve? Interviewers and mock-interview systems often struggle to produce realistic, scenario-based data engineering questions that go beyond textbook definitions and actually probe a candidate's hands-on experience with data warehouses, pipelines, and data quality incidents. ## Core Features & Use Cases - Topic Coverage: Provides structured question ladders across data warehouse layering, dimensional modeling, offline SQL optimization, real-time Flink pipelines, scheduling and task governance, data quality, lakehouse formats (Iceberg/Hudi/Paimon), metric governance, cost optimization, and CDC data ingestion. - Good vs Bad Question Patterns: Contrasts shallow definitional questions with scenario-based alternatives, including danger signals and expected signals for evaluating candidate answers. - Resume-Based Probing: Supplies follow-up hooks tied to resume claims such as real-time warehouses, performance optimization, and scheduling platforms. - Use Case: When a candidate's resume mentions building a real-time data warehouse with Flink, load this Skill to generate targeted follow-ups on state size, checkpoint intervals, late data handling, and past production incidents. ## Quick Start Load this Skill and ask it to generate interview questions for a candidate whose resume mentions Hive, Spark, and real-time data warehouse experience.
