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Data Engineer Interview Path

Master data engineering interviews with real-world use cases. Each scenario includes key topics, interview questions, and technical concepts you'll encounter at top tech companies.

8
Use Cases
40+
Interview Questions
8
Categories
100%
Industry-Standard
🌊

Building Real-Time Data Pipeline with Kafka

AdvancedStream Processing

Design and implement a real-time data pipeline using Apache Kafka for event streaming and processing.

🎯 Key Topics to Master:

✓Kafka Architecture & Components
✓Producer & Consumer Patterns
✓Stream Processing with Kafka Streams
✓Exactly-Once Semantics
✓Schema Management & Evolution
✓Partitioning & Scaling Strategies

💡 Common Interview Questions:

  • 1.How does Kafka ensure message delivery guarantees?
  • 2.What is the difference between Kafka and traditional message queues?
  • 3.How do you handle late-arriving data?
  • 4.What strategies handle schema evolution?
  • 5.How do you monitor and troubleshoot Kafka pipelines?

🔧 Technical Concepts:

Topics, partitions, and offsetsConsumer groups and rebalancingKafka Connect for data integrationAVRO and schema registryBackpressure handling
🏢

Designing a Data Warehouse with Snowflake/Redshift

AdvancedData Warehousing

Build a modern cloud data warehouse with dimensional modeling and optimization techniques.

🎯 Key Topics to Master:

✓Star Schema & Snowflake Schema Design
✓Slowly Changing Dimensions (SCD)
✓Data Partitioning & Clustering
✓Query Optimization Techniques
✓Materialized Views & Aggregations
✓Data Vault Modeling

💡 Common Interview Questions:

  • 1.What are the differences between fact and dimension tables?
  • 2.How do you handle slowly changing dimensions?
  • 3.What partitioning strategies improve query performance?
  • 4.When should you use materialized views?
  • 5.How do you optimize for analytical workloads?

🔧 Technical Concepts:

Columnar storage formatsDistribution keys and sort keysVacuum and analyze operationsWorkload managementCost optimization strategies
⚡

Batch ETL Pipeline with Apache Spark

AdvancedBig Data Processing

Implement large-scale batch data processing pipeline using Apache Spark with optimization best practices.

🎯 Key Topics to Master:

✓Spark Architecture (Driver, Executors)
✓RDD, DataFrame, and Dataset APIs
✓Data Transformations & Actions
✓Partition Management
✓Performance Tuning & Optimization
✓Integration with Data Lakes

💡 Common Interview Questions:

  • 1.What is the difference between transformation and action in Spark?
  • 2.How does Spark achieve fault tolerance?
  • 3.What causes data skew and how do you handle it?
  • 4.How do you optimize Spark job performance?
  • 5.When should you use broadcast joins?

🔧 Technical Concepts:

Lazy evaluation and DAGShuffle operations and optimizationCaching and persistence strategiesDynamic partition pruningAdaptive query execution
🗄️

Data Lake Architecture on AWS S3

IntermediateData Lake

Design a scalable data lake architecture with proper organization, governance, and access patterns.

🎯 Key Topics to Master:

✓Data Lake Zones (Raw, Processed, Curated)
✓File Formats (Parquet, ORC, Avro)
✓Partitioning Strategies
✓Data Cataloging with AWS Glue
✓Security & Access Control
✓Cost Optimization

💡 Common Interview Questions:

  • 1.What are the benefits of a data lake over a data warehouse?
  • 2.How do you organize data in a data lake?
  • 3.Why use columnar formats like Parquet?
  • 4.How do you handle data governance in a data lake?
  • 5.What are the challenges of schema-on-read?

🔧 Technical Concepts:

S3 storage classes and lifecycle policiesAWS Glue crawlers and ETLAthena for serverless queriesLake Formation for securityData compaction strategies
🔄

Change Data Capture (CDC) Implementation

AdvancedData Integration

Implement real-time data replication from operational databases to analytics platforms using CDC.

🎯 Key Topics to Master:

✓CDC Patterns & Approaches
✓Debezium for MySQL/PostgreSQL
✓Handling Schema Changes
✓Event Ordering & Consistency
✓Incremental Data Loading
✓Conflict Resolution

💡 Common Interview Questions:

  • 1.What are the different CDC approaches?
  • 2.How does log-based CDC work?
  • 3.How do you handle large initial snapshots?
  • 4.What challenges arise with schema evolution?
  • 5.How do you ensure data consistency?

🔧 Technical Concepts:

Transaction logs and binlogsKafka Connect and DebeziumWatermarking for incremental loadsIdempotent processingBackfill strategies
✅

Data Quality & Validation Framework

IntermediateData Quality

Build a comprehensive data quality framework with automated validation, monitoring, and alerting.

🎯 Key Topics to Master:

✓Data Quality Dimensions
✓Validation Rules & Constraints
✓Data Profiling & Statistics
✓Anomaly Detection
✓Data Lineage Tracking
✓Quality Metrics & SLAs

💡 Common Interview Questions:

  • 1.What are the key dimensions of data quality?
  • 2.How do you detect data quality issues early?
  • 3.What is data lineage and why is it important?
  • 4.How do you handle bad data in pipelines?
  • 5.What tools help automate data quality checks?

🔧 Technical Concepts:

Great Expectations frameworkDeequ for Spark validationStatistical outlier detectionData profiling techniquesCircuit breaker patterns
🎼

Orchestrating Complex Data Workflows with Airflow

AdvancedWorkflow Orchestration

Design and manage complex data workflows with dependencies, retries, and monitoring using Apache Airflow.

🎯 Key Topics to Master:

✓DAG Design & Best Practices
✓Task Dependencies & Branching
✓Scheduling & Backfilling
✓Error Handling & Retries
✓Dynamic DAG Generation
✓Monitoring & Alerting

💡 Common Interview Questions:

  • 1.What are best practices for designing Airflow DAGs?
  • 2.How do you handle task failures and retries?
  • 3.What is the difference between start_date and execution_date?
  • 4.How do you test Airflow DAGs?
  • 5.What are the limitations of Airflow?

🔧 Technical Concepts:

Operators, sensors, and hooksXCom for task communicationExecutors (Local, Celery, Kubernetes)SLA monitoringAirflow variables and connections
🏠

Building a Lakehouse with Delta Lake

AdvancedLakehouse Architecture

Implement a lakehouse architecture combining data lake flexibility with data warehouse reliability.

🎯 Key Topics to Master:

✓ACID Transactions on Data Lakes
✓Time Travel & Versioning
✓Schema Evolution & Enforcement
✓Upserts & Deletes
✓Z-Ordering & Data Skipping
✓Streaming & Batch Unification

💡 Common Interview Questions:

  • 1.What problems does Delta Lake solve?
  • 2.How does Delta Lake provide ACID guarantees?
  • 3.What is time travel and when is it useful?
  • 4.How do you optimize Delta tables?
  • 5.What are the differences between Delta, Iceberg, and Hudi?

🔧 Technical Concepts:

Transaction log and metadataOPTIMIZE and VACUUM commandsZ-order clusteringMerge operations for upsertsChange data feed

📚 How to Use This Path

1. Study Each Use Case

Go through each scenario systematically. Understand the data flow, architecture, and tradeoffs.

2. Practice Interview Questions

Prepare answers for each question. Focus on explaining data engineering principles and best practices.

3. Build Data Pipelines

Implement hands-on projects using Spark, Kafka, or Airflow. Document your design decisions.

4. Master Data Technologies

Gain practical experience with big data tools. Be ready to discuss performance optimization and scaling.