Data Engineer Interview Questions

Data Pipeline Architecture

Understanding the core components of data pipelines:

• Data Sources: Databases, APIs, files, streams, and other origin points
• Ingestion Layer: Tools and processes for collecting and importing data
• Processing Layer: Transformation, enrichment, and computation systems
• Storage Layer: Data warehouses, data lakes, and specialized stores
• Serving Layer: Query engines, APIs, and interfaces for data consumption

ETL vs. ELT Processes

Different approaches to data integration:

• Extract-Transform-Load (ETL): Traditional approach with transformation before loading
• Extract-Load-Transform (ELT): Modern approach leveraging target system for transformations
• Batch Processing: Handling data in scheduled, discrete chunks
• Stream Processing: Continuous processing of data as it arrives
• Change Data Capture (CDC): Tracking and processing data changes at the source

Data Storage Systems

Different types of data storage technologies:

• Relational Databases: PostgreSQL, MySQL, Oracle, SQL Server
• Data Warehouses: Snowflake, Redshift, BigQuery, Synapse
• Data Lakes: S3, ADLS, GCS with formats like Parquet, ORC, Avro
• NoSQL Databases: MongoDB, Cassandra, DynamoDB, Couchbase
• Specialized Stores: Time-series databases, graph databases, search engines

Data Modeling & Schema Design

Structuring data for efficient storage and retrieval:

• Dimensional Modeling: Star and snowflake schemas for analytics
• Data Vault Modeling: Flexible approach for enterprise data warehousing
• Normalization: 1NF through 5NF for relational databases
• Denormalization: Trading redundancy for performance
• Schema Evolution: Managing changing data structures over time

Batch Processing

Processing data in scheduled intervals:

• MapReduce: Distributed processing paradigm for large datasets
• Hadoop Ecosystem: HDFS, YARN, Hive, Pig, and related technologies
• Spark Batch: In-memory distributed processing with Spark
• Workflow Orchestration: Managing dependencies and scheduling
• Incremental Processing: Efficiently processing only new or changed data

Stream Processing

Processing data in real-time as it arrives:

• Event-Driven Architecture: Responding to data events as they occur
• Stream Processing Frameworks: Kafka Streams, Flink, Spark Streaming
• Windowing: Time-based, count-based, and session windows
• State Management: Handling stateful operations in streaming
• Exactly-Once Processing: Ensuring data is processed precisely once

Data Quality & Governance

Ensuring data reliability and compliance:

• Data Validation: Schema validation, constraint checking, anomaly detection
• Data Lineage: Tracking data origin and transformations
• Data Catalogs: Metadata management and discovery
• Master Data Management: Maintaining consistent reference data
• Data Privacy: Anonymization, encryption, access controls

Performance Optimization

Improving data processing efficiency:

• Query Optimization: Indexing, partitioning, and query tuning
• Distributed Computing: Parallelization and data locality
• Caching Strategies: Materialized views, result caching, in-memory caching
• Data Compression: Columnar formats, compression algorithms
• Resource Management: CPU, memory, and I/O optimization

Data Pipeline Design

• Design a data pipeline that processes e-commerce transactions in real-time and generates hourly sales reports.
• How would you handle late-arriving data in a pipeline that produces daily aggregations?
• Explain the trade-offs between batch and streaming data processing. When would you choose one over the other?
• How would you design a pipeline that needs to join data from multiple sources with different update frequencies?
• Describe how you would implement a data pipeline that needs to maintain historical versions of slowly changing dimensions.

Big Data Technologies

• Compare and contrast Hadoop and Spark. What are the strengths and weaknesses of each?
• Explain how Spark achieves better performance than traditional MapReduce.
• How would you optimize a Spark job that's running slowly?
• Describe the architecture of a Kafka-based streaming system and how you would ensure data reliability.
• How would you handle schema evolution in a data lake environment?

Data Warehousing & SQL

• Design a star schema for tracking customer purchases across multiple retail channels.
• How would you optimize a slow-running SQL query in a data warehouse?
• Explain the concept of data partitioning and when you would use it.
• Compare and contrast columnar and row-based storage formats for analytical workloads.
• How would you implement slowly changing dimensions in a data warehouse?

Data Quality & Testing

• How would you ensure data quality in a pipeline that processes millions of records daily?
• Describe your approach to testing data pipelines.
• How would you handle missing or corrupted data in a production pipeline?
• Explain how you would implement data validation checks at different stages of a pipeline.
• How would you monitor data quality metrics over time?

Cloud Data Platforms

• Compare AWS, Azure, and GCP for data engineering workloads.
• How would you design a cost-effective data lake on cloud storage?
• Explain the architecture of a serverless data processing pipeline on your preferred cloud platform.
• How would you handle data security and compliance requirements in a cloud environment?
• Describe strategies for optimizing cloud data warehouse performance and cost.

Data Processing Frameworks

• Apache Spark: Unified analytics engine for large-scale data processing
• Apache Hadoop: Distributed storage and processing framework
• Apache Flink: Stream processing framework with batch capabilities
• Apache Beam: Unified programming model for batch and streaming
• Dask: Parallel computing library for Python

Data Integration & ETL

• Apache Airflow: Workflow orchestration platform
• Apache NiFi: Dataflow management and automation
• dbt (data build tool): SQL-based transformation tool
• Fivetran/Stitch: Managed data integration services
• Talend/Informatica: Enterprise data integration platforms

Streaming & Messaging

• Apache Kafka: Distributed event streaming platform
• Apache Pulsar: Distributed messaging and streaming platform
• Amazon Kinesis: Real-time streaming data service
• Google Pub/Sub: Messaging and event ingestion service
• RabbitMQ: Message broker for applications

Data Storage & Warehousing

• Snowflake: Cloud data platform with separation of storage and compute
• Amazon Redshift: Cloud data warehouse service
• Google BigQuery: Serverless, highly scalable data warehouse
• Apache Hive: Data warehouse software for Hadoop
• Delta Lake/Iceberg/Hudi: Lakehouse table formats

Data Quality & Observability

• Great Expectations: Data validation and documentation
• Apache Griffin: Big data quality solution
• Monte Carlo/Datadog: Data observability platforms
• dbt Tests: Testing framework within dbt
• Prometheus/Grafana: Monitoring and visualization

Business Intelligence & Analytics

Supporting data-driven decision making:

• Enterprise data warehouse implementation
• Self-service analytics platforms
• Operational reporting and dashboards
• OLAP systems and multidimensional analysis
• Embedded analytics for applications

Machine Learning & AI

Enabling advanced analytics and automation:

• Feature engineering pipelines
• Model training data preparation
• ML model serving infrastructure
• Real-time prediction systems
• MLOps and model lifecycle management

Customer Data Platforms

Unifying customer data across touchpoints:

• Identity resolution and customer 360
• Behavioral data collection and processing
• Real-time segmentation engines
• Marketing automation data pipelines
• Privacy-compliant data management

IoT & Sensor Data

Processing data from connected devices:

• High-volume sensor data ingestion
• Time-series data processing and storage
• Edge computing integration
• Real-time monitoring and alerting
• Predictive maintenance systems

Financial Data Systems

Managing critical financial information:

• Transaction processing systems
• Regulatory reporting pipelines
• Risk analysis data platforms
• Fraud detection systems
• Algorithmic trading infrastructure