💻High Prioritymedium15-20 minutes
Do you have hands-on experience preparing data or building pipelines for ML systems?
technicaldata-pipelinedata-engineeringfeature-engineeringhigh-priority
🎯 What Interviewers Are Looking For
- ✓Understanding that data work is 80% of ML
- ✓Experience with data cleaning, transformation, feature engineering
- ✓Knowledge of data pipeline tools and best practices
- ✓Awareness of data quality issues and how they affect models
📋 STAR Framework Guide
Structure your answer using this framework:
S - Situation
What data challenge or pipeline did you work on?
T - Task
What was required? What problems needed solving?
A - Action
How did you build/improve the pipeline? What tools did you use?
R - Result
What was the impact on data quality or model performance?
💬 Example Answer
⚠️ Pitfalls to Avoid
- ✗Claiming you just "loaded a CSV and trained a model" without any data work
- ✗Not acknowledging how much time data preparation actually takes
- ✗Focusing only on modeling without discussing data challenges
- ✗Not understanding data leakage or train/test contamination
- ✗Not being specific about tools and techniques you used
- ✗Ignoring data quality issues and their impact on models
💡 Pro Tips
- ✓Emphasize that you understand data work is most of ML (80/20 rule)
- ✓Give specific examples: what issues you found, how you fixed them
- ✓Mention tools: pandas, sklearn pipelines, data validation libraries
- ✓Show you think about data quality, not just model accuracy
- ✓Discuss train/val/test splits and avoiding data leakage
- ✓If limited experience: mention what you'd want to learn (Airflow, Spark, dbt)
- ✓Connect data quality to model performance with concrete examples
- ✓Show iterative mindset: data prep → modeling → error analysis → better data prep
🔄 Common Follow-up Questions
- →How do you handle missing data?
- →What's your approach to feature engineering?
- →Have you worked with streaming data or batch processing?
- →How do you detect data drift in production?
- →What tools have you used for data pipeline orchestration?
- →How do you ensure train/test splits don't leak data?
- →Have you worked with large-scale data that doesn't fit in memory?
- →How do you handle class imbalance?
🎤 Practice Your Answer
0:00
Target: 2-3 minutes
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