Lilly Tech Systems
Data Pipeline Coding Challenges
Practical data engineering problems from ML and data engineer interviews. Every challenge includes a realistic dataset, problem statement, and complete Python solution covering ETL, validation, streaming, feature engineering, and performance optimization.
Your Learning Path
Follow these lessons in order to build strong data pipeline skills for engineering interviews, or jump to any topic you need to practice.
1. Data Pipeline Coding in Interviews
What to expect in data pipeline interviews, common patterns, how companies test ETL skills, and a framework for solving pipeline problems systematically.
2. ETL Challenges
5 challenges: CSV parsing with edge cases, JSON flattening, data transformation pipelines, schema mapping between systems, and incremental load logic.
3. Data Validation Challenges
5 challenges: schema validation, null checking strategies, range validation, referential integrity checks, and duplicate detection algorithms.
4. Streaming Data Challenges
5 challenges: sliding window aggregation, event deduplication, late arrival handling, session window detection, and watermark-based processing.
5. Feature Engineering Challenges
5 challenges: time-based features, categorical encoding pipelines, text feature extraction, aggregation features, and interaction feature generation.
6. Performance Optimization
5 challenges: chunked processing, parallel execution, memory-efficient operations, caching strategies, and batch vs streaming trade-offs.
7. Patterns & Tips
Data pipeline design patterns, testing strategies for pipelines, idempotency, monitoring, and a FAQ accordion covering common interview questions.
What You'll Learn
By the end of this course, you will be able to:
Build Production ETL Pipelines
Parse messy data formats, flatten nested structures, map schemas between systems, and implement incremental load strategies used in real data platforms.
Validate Data at Scale
Implement schema checks, null handling, range constraints, referential integrity, and deduplication logic that production data pipelines require.
Handle Streaming Data
Solve windowed aggregation, event deduplication, late arrival, and sessionization problems that streaming infrastructure engineers face daily.
Optimize Pipeline Performance
Apply chunking, parallelism, memory optimization, and caching techniques to process large datasets efficiently within time and resource constraints.