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