Data Engineer - Expert Guide
Design and maintain scalable, reliable data infrastructure for enterprise analytics.
🎯 Expert Coverage
Data Pipeline Architecture
Pipeline Design
- Batch vs stream processing
- ETL vs ELT patterns
- Data ingestion strategies
- Transformation logic
- Data quality validation
- Error handling and recovery
- Idempotency and deduplication
Orchestration
- Airflow DAGs and monitoring
- Prefect for modern workflows
- dbt for analytics engineering
- Temporal for distributed workflows
- Scheduler best practices
Database Systems
SQL Databases
- PostgreSQL fundamentals and optimization
- MySQL and MariaDB
- Microsoft SQL Server
- Query optimization and indexing
- Transactions and ACID properties
- Partitioning strategies
- Replication and high availability
NoSQL Databases
- MongoDB document model
- Cassandra distributed architecture
- Redis for caching
- DynamoDB serverless database
- Graph databases (Neo4j)
- Time-series databases
Data Warehouses
- Snowflake architecture
- BigQuery serverless analytics
- Redshift data warehouse
- DataLake vs DataWarehouse
- Star and snowflake schemas
- Slowly changing dimensions (SCD)
- Fact and dimension tables
Data Processing
Python for Data Engineering
- Pandas for data manipulation
- Polars for performance
- PySpark for big data
- Dask for parallel computing
- SQL with sqlalchemy
- Data profiling and quality checks
Apache Spark
- RDD, DataFrame, Dataset APIs
- Spark SQL and optimization
- Structured streaming
- Machine learning with MLlib
- Performance tuning
- Cluster management (Yarn, Kubernetes)
SQL Mastery
- Query optimization
- Window functions
- Common Table Expressions (CTEs)
- Complex joins and set operations
- Stored procedures and triggers
- Query execution plans
Data Modeling
Relational Design
- Normalization principles (1NF-3NF, BCNF)
- Denormalization for performance
- Relationships and referential integrity
- Key selection (natural vs surrogate)
Dimensional Modeling
- Fact tables (transactional, periodic snapshot)
- Dimension tables and SCD strategies
- Conformed dimensions
- Kimball methodology
- Star schema design
Semi-structured Data
- JSON and JSON schema
- Avro and Protocol Buffers
- Parquet columnar format
- Data versioning
Streaming Data
Stream Processing
- Kafka for distributed streaming
- Apache Flink stream processor
- Spark Streaming (micro-batches)
- Real-time transformations
- Stateful processing
- Watermarking and windowing
- Exactly-once semantics
Event Architecture
- Event sourcing patterns
- CQRS (Command Query)
- Event-driven architecture
- Topic partitioning
- Consumer groups
Data Quality
Validation Framework
- Great Expectations
- dbt tests
- Custom validation rules
- Anomaly detection
- Data profiling
- Reconciliation
Data Governance
- Data lineage and tracking
- Metadata management
- Access control and RBAC
- Data cataloging
- Compliance (GDPR, CCPA)
- Data retention policies
Performance Tuning
Optimization Strategies
- Indexing strategies
- Query optimization
- Materialized views
- Incremental loading
- Partition pruning
- Compression techniques
Monitoring & Observability
- Query performance monitoring
- Data freshness tracking
- Cost optimization
- Resource utilization
- Alert configuration
📚 Learning Path (12 Weeks)
Weeks 1-3: Foundations
- SQL fundamentals and optimization
- Database design and modeling
- Python basics
- ETL concepts
Weeks 4-6: Core Tools
- Apache Spark essentials
- Data pipeline development
- dbt fundamentals
- Orchestration basics
Weeks 7-9: Advanced Topics
- Streaming systems
- Data warehousing design
- Performance optimization
- Data governance
Weeks 10-12: Production
- Real-time pipelines
- Monitoring and alerting
- Cost optimization
- Capstone project
🛠️ Technology Stack
SQL: PostgreSQL, Snowflake, BigQuery, Redshift
Python: Pandas, PySpark, Polars, SQLAlchemy
Processing: Apache Spark, Flink, Dask, Beam
Orchestration: Airflow, dbt, Prefect, Temporal
Streaming: Kafka, AWS Kinesis, Pub/Sub
Data Warehouse: Snowflake, BigQuery, Redshift
Monitoring: DataDog, New Relic, Splunk
Version Control: Git, dbt Cloud
🎯 Real-World Projects
Beginner: ETL from CSV to database, simple data pipeline
Intermediate: Multi-source data warehouse, real-time analytics
Advanced: Event streaming system, data mesh architecture
💼 Career Progression
| Level | Skills | Experience | Salary |
|---|
| Junior Data Engineer | SQL, basic ETL | 0-2 years | $90K-$130K |
| Data Engineer | Spark, Airflow, advanced SQL | 2-5 years | $130K-$180K |
| Senior Data Engineer | Architecture, optimization | 5-8+ years | $180K-$250K |
| Staff Engineer | System design, strategy | 8+ years | $250K-$400K+ |
🚀 When to Invoke
- Pipeline design and architecture
- Database optimization
- Data modeling strategies
- ETL/ELT workflow design
- Performance bottleneck identification
- Data warehouse design
- Streaming system implementation
- Data quality strategy
- Cost optimization
- Governance framework