Senior Data Engineer#

Core Capabilities#

• Batch Pipeline Orchestration - Design and implement production-ready ETL/ELT pipelines with Airflow, intelligent dependency resolution, retry logic, and comprehensive monitoring
• Real-Time Streaming - Build event-driven streaming pipelines with Kafka, Flink, Kinesis, and Spark Streaming with exactly-once semantics and sub-second latency
• Data Quality Management - Comprehensive batch and streaming data quality validation covering completeness, accuracy, consistency, timeliness, and validity
• Streaming Quality Monitoring - Track consumer lag, data freshness, schema drift, throughput, and dead letter queue rates for streaming pipelines
• Performance Optimization - Analyze and optimize pipeline performance with query optimization, Spark tuning, and cost analysis recommendations

Key Workflows#

Workflow 1: Build ETL Pipeline#

Time: 2-4 hours

Steps:

1. Design pipeline architecture using Lambda, Kappa, or Medallion pattern
2. Configure YAML pipeline definition with sources, transformations, targets
3. Generate Airflow DAG with pipeline_orchestrator.py
4. Define data quality validation rules
5. Deploy and configure monitoring/alerting

Expected Output: Production-ready ETL pipeline with 99%+ success rate, automated quality checks, and comprehensive monitoring

Workflow 2: Build Real-Time Streaming Pipeline#

Time: 3-5 days

Steps:

1. Select streaming architecture (Kappa vs Lambda) based on requirements
2. Configure streaming pipeline YAML (sources, processing, sinks, quality)
3. Generate Kafka configurations with kafka_config_generator.py
4. Generate Flink/Spark job scaffolding with stream_processor.py
5. Deploy and monitor with streaming_quality_validator.py

Expected Output: Streaming pipeline processing 10K+ events/sec with P99 latency < 1s, exactly-once delivery, and real-time quality monitoring

World-class data engineering for production-grade data systems, scalable pipelines, and enterprise data platforms.

Overview#

This skill provides comprehensive expertise in data engineering fundamentals through advanced production patterns. From designing medallion architectures to implementing real-time streaming pipelines, it covers the full spectrum of modern data engineering including ETL/ELT design, data quality frameworks, pipeline orchestration, and DataOps practices.

What This Skill Provides:

• Production-ready pipeline templates (Airflow, Spark, dbt)
• Comprehensive data quality validation framework
• Performance optimization and cost analysis tools
• Data architecture patterns (Lambda, Kappa, Medallion)
• Complete DataOps CI/CD workflows

Best For:

• Building scalable data pipelines for enterprise systems
• Implementing data quality and governance frameworks
• Optimizing ETL performance and cloud costs
• Designing modern data architectures (lake, warehouse, lakehouse)
• Production ML/AI data infrastructure

Quick Start#

Pipeline Orchestration#

bash

# Generate Airflow DAG from configuration
python scripts/pipeline_orchestrator.py --config pipeline_config.yaml --output dags/

# Validate pipeline configuration
python scripts/pipeline_orchestrator.py --config pipeline_config.yaml --validate

# Use incremental load template
python scripts/pipeline_orchestrator.py --template incremental --output dags/

Data Quality Validation#

bash

# Validate CSV file with quality checks
python scripts/data_quality_validator.py --input data/sales.csv --output report.html

# Validate database table with custom rules
python scripts/data_quality_validator.py \
    --connection postgresql://user:pass@host/db \
    --table sales_transactions \
    --rules rules/sales_validation.yaml \
    --threshold 0.95

Performance Optimization#

bash

# Analyze pipeline performance and get recommendations
python scripts/etl_performance_optimizer.py \
    --airflow-db postgresql://host/airflow \
    --dag-id sales_etl_pipeline \
    --days 30 \
    --optimize

# Analyze Spark job performance
python scripts/etl_performance_optimizer.py \
    --spark-history-server http://spark-history:18080 \
    --app-id app-20250115-001

Real-Time Streaming#

bash

# Validate streaming pipeline configuration
python scripts/stream_processor.py --config streaming_config.yaml --validate

# Generate Kafka topic and client configurations
python scripts/kafka_config_generator.py \
    --topic user-events \
    --partitions 12 \
    --replication 3 \
    --output kafka/topics/

# Generate exactly-once producer configuration
python scripts/kafka_config_generator.py \
    --producer \
    --profile exactly-once \
    --output kafka/producer.properties

# Generate Flink job scaffolding
python scripts/stream_processor.py \
    --config streaming_config.yaml \
    --mode flink \
    --generate \
    --output flink-jobs/

# Monitor streaming quality
python scripts/streaming_quality_validator.py \
    --lag --consumer-group events-processor --threshold 10000 \
    --freshness --topic processed-events --max-latency-ms 5000 \
    --output streaming-health-report.html

Core Workflows#

1. Building Production Data Pipelines#

Steps:

1. Design Architecture: Choose pattern (Lambda, Kappa, Medallion) based on requirements
2. Configure Pipeline: Create YAML configuration with sources, transformations, targets
3. Generate DAG: python scripts/pipeline_orchestrator.py --config config.yaml
4. Add Quality Checks: Define validation rules for data quality
5. Deploy & Monitor: Deploy to Airflow, configure alerts, track metrics

Pipeline Patterns: See frameworks.md for Lambda Architecture, Kappa Architecture, Medallion Architecture (Bronze/Silver/Gold), and Microservices Data patterns.

Templates: See templates.md for complete Airflow DAG templates, Spark job templates, dbt models, and Docker configurations.

2. Data Quality Management#

Steps:

1. Define Rules: Create validation rules covering completeness, accuracy, consistency
2. Run Validation: python scripts/data_quality_validator.py --rules rules.yaml
3. Review Results: Analyze quality scores and failed checks
4. Integrate CI/CD: Add validation to pipeline deployment process
5. Monitor Trends: Track quality scores over time

Quality Framework: See frameworks.md for complete Data Quality Framework covering all dimensions (completeness, accuracy, consistency, timeliness, validity).

Validation Templates: See templates.md for validation configuration examples and Python API usage.

3. Data Modeling & Transformation#

Steps:

1. Choose Modeling Approach: Dimensional (Kimball), Data Vault 2.0, or One Big Table
2. Design Schema: Define fact tables, dimensions, and relationships
3. Implement with dbt: Create staging, intermediate, and mart models
4. Handle SCD: Implement slowly changing dimension logic (Type 1/2/3)
5. Test & Deploy: Run dbt tests, generate documentation, deploy

Modeling Patterns: See frameworks.md for Dimensional Modeling (Kimball), Data Vault 2.0, One Big Table (OBT), and SCD implementations.

dbt Templates: See templates.md for complete dbt model templates including staging, intermediate, fact tables, and SCD Type 2 logic.

4. Performance Optimization#

Steps:

1. Profile Pipeline: Run performance analyzer on recent pipeline executions
2. Identify Bottlenecks: Review execution time breakdown and slow tasks
3. Apply Optimizations: Implement recommendations (partitioning, indexing, batching)
4. Tune Spark Jobs: Optimize memory, parallelism, and shuffle settings
5. Measure Impact: Compare before/after metrics, track cost savings

Optimization Strategies: See frameworks.md for performance best practices including partitioning strategies, query optimization, and Spark tuning.

Analysis Tools: See tools.md for complete documentation on etl_performance_optimizer.py with query analysis and Spark tuning.

5. Building Real-Time Streaming Pipelines#

Steps:

1. Architecture Selection: Choose Kappa (streaming-only) or Lambda (batch + streaming) architecture
2. Configure Pipeline: Create YAML config with sources, processing engine, sinks, quality thresholds
3. Generate Kafka Configs: python scripts/kafka_config_generator.py --topic events --partitions 12
4. Generate Job Scaffolding: python scripts/stream_processor.py --mode flink --generate
5. Deploy Infrastructure: Use Docker Compose for local dev, Kubernetes for production
6. Monitor Quality: python scripts/streaming_quality_validator.py --lag --freshness --throughput

Streaming Patterns: See frameworks.md for stateful processing, stream joins, windowing, exactly-once semantics, and CDC patterns.

Templates: See templates.md for Flink DataStream jobs, Kafka Streams applications, PyFlink templates, and Docker Compose configurations.

Python Tools#

pipeline_orchestrator.py#

Automated Airflow DAG generation with intelligent dependency resolution and monitoring.

Key Features:

• Generate production-ready DAGs from YAML configuration
• Automatic task dependency resolution
• Built-in retry logic and error handling
• Multi-source support (PostgreSQL, S3, BigQuery, Snowflake)
• Integrated quality checks and alerting

Usage:

bash

# Basic DAG generation
python scripts/pipeline_orchestrator.py --config pipeline_config.yaml --output dags/

# With validation
python scripts/pipeline_orchestrator.py --config config.yaml --validate

# From template
python scripts/pipeline_orchestrator.py --template incremental --output dags/

Complete Documentation: See tools.md for full configuration options, templates, and integration examples.

data_quality_validator.py#

Comprehensive data quality validation framework with automated checks and reporting.

Capabilities:

• Multi-dimensional validation (completeness, accuracy, consistency, timeliness, validity)
• Great Expectations integration
• Custom business rule validation
• HTML/PDF report generation
• Anomaly detection
• Historical trend tracking

Usage:

bash

# Validate with custom rules
python scripts/data_quality_validator.py \
    --input data/sales.csv \
    --rules rules/sales_validation.yaml \
    --output report.html

# Database table validation
python scripts/data_quality_validator.py \
    --connection postgresql://host/db \
    --table sales_transactions \
    --threshold 0.95

Complete Documentation: See tools.md for rule configuration, API usage, and integration patterns.

etl_performance_optimizer.py#

Pipeline performance analysis with actionable optimization recommendations.

Capabilities:

• Airflow DAG execution profiling
• Bottleneck detection and analysis
• SQL query optimization suggestions
• Spark job tuning recommendations
• Cost analysis and optimization
• Historical performance trending

Usage:

bash

# Analyze Airflow DAG
python scripts/etl_performance_optimizer.py \
    --airflow-db postgresql://host/airflow \
    --dag-id sales_etl_pipeline \
    --days 30 \
    --optimize

# Spark job analysis
python scripts/etl_performance_optimizer.py \
    --spark-history-server http://spark-history:18080 \
    --app-id app-20250115-001

Complete Documentation: See tools.md for profiling options, optimization strategies, and cost analysis.

stream_processor.py#

Streaming pipeline configuration generator and validator for Kafka, Flink, and Kinesis.

Capabilities:

• Multi-platform support (Kafka, Flink, Kinesis, Spark Streaming)
• Configuration validation with best practice checks
• Flink/Spark job scaffolding generation
• Kafka topic configuration generation
• Docker Compose for local streaming stacks
• Exactly-once semantics configuration

Usage:

bash

# Validate configuration
python scripts/stream_processor.py --config streaming_config.yaml --validate

# Generate Kafka configurations
python scripts/stream_processor.py --config streaming_config.yaml --mode kafka --generate

# Generate Flink job scaffolding
python scripts/stream_processor.py --config streaming_config.yaml --mode flink --generate --output flink-jobs/

# Generate Docker Compose for local development
python scripts/stream_processor.py --config streaming_config.yaml --mode docker --generate

Complete Documentation: See tools.md for configuration format, validation checks, and generated outputs.

streaming_quality_validator.py#

Real-time streaming data quality monitoring with comprehensive health scoring.

Capabilities:

• Consumer lag monitoring with thresholds
• Data freshness validation (P50/P95/P99 latency)
• Schema drift detection
• Throughput analysis (events/sec, bytes/sec)
• Dead letter queue rate monitoring
• Overall quality scoring with recommendations
• Prometheus metrics export

Usage:

bash

# Monitor consumer lag
python scripts/streaming_quality_validator.py \
    --lag --consumer-group events-processor --threshold 10000

# Monitor data freshness
python scripts/streaming_quality_validator.py \
    --freshness --topic processed-events --max-latency-ms 5000

# Full quality validation
python scripts/streaming_quality_validator.py \
    --lag --freshness --throughput --dlq \
    --output streaming-health-report.html

Complete Documentation: See tools.md for all monitoring dimensions and integration patterns.

kafka_config_generator.py#

Production-grade Kafka configuration generator with performance and security profiles.

Capabilities:

• Topic configuration (partitions, replication, retention, compaction)
• Producer profiles (high-throughput, exactly-once, low-latency, ordered)
• Consumer profiles (exactly-once, high-throughput, batch)
• Kafka Streams configuration with state store tuning
• Security configuration (SASL-PLAIN, SASL-SCRAM, mTLS)
• Kafka Connect source/sink configurations
• Multiple output formats (properties, YAML, JSON)

Usage:

bash

# Generate topic configuration
python scripts/kafka_config_generator.py \
    --topic user-events --partitions 12 --replication 3 --retention-hours 168

# Generate exactly-once producer
python scripts/kafka_config_generator.py \
    --producer --profile exactly-once --transactional-id producer-001

# Generate Kafka Streams config
python scripts/kafka_config_generator.py \
    --streams --application-id events-processor --exactly-once

Complete Documentation: See tools.md for all profiles, security options, and Connect configurations.

Reference Documentation#

Frameworks ( frameworks.md)#

Comprehensive data engineering frameworks and patterns:

• Architecture Patterns: Lambda, Kappa, Medallion, Microservices data architecture
• Data Modeling: Dimensional (Kimball), Data Vault 2.0, One Big Table
• ETL/ELT Patterns: Full load, incremental load, CDC, SCD, idempotent pipelines
• Data Quality: Complete framework covering all quality dimensions
• DataOps: CI/CD for data pipelines, testing strategies, monitoring
• Orchestration: Airflow DAG patterns, backfill strategies
• Real-Time Streaming: Stateful processing, stream joins, windowing strategies, exactly-once semantics, event time processing, watermarks, backpressure, Apache Flink patterns, AWS Kinesis patterns, CDC for streaming
• Governance: Data catalog, lineage tracking, access control

Templates ( templates.md)#

Production-ready code templates and examples:

• Airflow DAGs: Complete ETL DAG, incremental load, dynamic task generation
• Spark Jobs: Batch processing, streaming, optimized configurations
• dbt Models: Staging, intermediate, fact tables, dimensions with SCD Type 2
• SQL Patterns: Incremental merge (upsert), deduplication, date spine, window functions
• Python Pipelines: Data quality validation class, retry decorators, error handling
• Real-Time Streaming: Apache Flink DataStream jobs (Java), Kafka Streams applications, PyFlink jobs, AWS Kinesis consumers, Docker Compose for streaming stack
• Kafka Configs: Producer/consumer properties templates, topic configurations, security configurations
• Docker: Dockerfiles for data pipelines, Docker Compose for local development including streaming stack (Kafka, Flink, Schema Registry)
• Configuration: dbt project config, Spark configuration, Airflow variables, streaming pipeline YAML
• Testing: pytest fixtures, integration tests, data quality tests

Tools ( tools.md)#

Python automation tool documentation:

• pipeline_orchestrator.py: Complete usage guide, configuration format, DAG templates
• data_quality_validator.py: Validation rules, dimension checks, Great Expectations integration
• etl_performance_optimizer.py: Performance analysis, query optimization, Spark tuning
• stream_processor.py: Streaming pipeline configuration, validation, job scaffolding generation
• streaming_quality_validator.py: Consumer lag, data freshness, schema drift, throughput monitoring
• kafka_config_generator.py: Topic, producer, consumer, Kafka Streams, and Connect configurations
• Integration Patterns: Airflow, dbt, CI/CD, monitoring systems, Prometheus
• Best Practices: Configuration management, error handling, performance, monitoring, streaming quality

Tech Stack#

Core Technologies:

• Languages: Python 3.8+, SQL, Scala (Spark), Java (Flink)
• Orchestration: Apache Airflow, Prefect, Dagster
• Batch Processing: Apache Spark, dbt, Pandas
• Stream Processing: Apache Kafka, Apache Flink, Kafka Streams, Spark Structured Streaming, AWS Kinesis
• Storage: PostgreSQL, BigQuery, Snowflake, Redshift, S3, GCS
• Schema Management: Confluent Schema Registry, AWS Glue Schema Registry
• Containerization: Docker, Kubernetes
• Monitoring: Datadog, Prometheus, Grafana, Kafka UI

Data Platforms:

• Cloud Data Warehouses: Snowflake, BigQuery, Redshift
• Data Lakes: Delta Lake, Apache Iceberg, Apache Hudi
• Streaming Platforms: Apache Kafka, AWS Kinesis, Google Pub/Sub, Azure Event Hubs
• Stream Processing Engines: Apache Flink, Kafka Streams, Spark Structured Streaming
• Workflow: Airflow, Prefect, Dagster

Integration Points#

This skill integrates with:

• Orchestration: Airflow, Prefect, Dagster for workflow management
• Transformation: dbt for SQL transformations and testing
• Quality: Great Expectations for data validation
• Monitoring: Datadog, Prometheus for pipeline monitoring
• BI Tools: Looker, Tableau, Power BI for analytics
• ML Platforms: MLflow, Kubeflow for ML pipeline integration
• Version Control: Git for pipeline code and configuration

See tools.md for detailed integration patterns and examples.

Best Practices#

Pipeline Design:

1. Idempotent operations for safe reruns
2. Incremental processing where possible
3. Clear data lineage and documentation
4. Comprehensive error handling
5. Automated recovery mechanisms

Data Quality:

1. Define quality rules early
2. Validate at every pipeline stage
3. Automate quality monitoring
4. Track quality trends over time
5. Block bad data from downstream

Performance:

1. Partition large tables by date/region
2. Use columnar formats (Parquet, ORC)
3. Leverage predicate pushdown
4. Optimize for your query patterns
5. Monitor and tune regularly

Operations:

1. Version control everything
2. Automate testing and deployment
3. Implement comprehensive monitoring
4. Document runbooks for incidents
5. Regular performance reviews

Performance Targets#

Batch Pipeline Execution:

• P50 latency: < 5 minutes (hourly pipelines)
• P95 latency: < 15 minutes
• Success rate: > 99%
• Data freshness: < 1 hour behind source

Streaming Pipeline Execution:

• Throughput: 10K+ events/second sustained
• End-to-end latency: P99 < 1 second
• Consumer lag: < 10K records behind
• Exactly-once delivery: Zero duplicates or losses

Data Quality (Batch):

• Quality score: > 95%
• Completeness: > 99%
• Timeliness: < 2 hours data lag
• Zero critical failures

Streaming Quality:

• Data freshness: P95 < 5 minutes from event generation
• Late data rate: < 5% outside watermark window
• Dead letter queue rate: < 1%
• Schema compatibility: 100% backward/forward compatible changes

Cost Efficiency:

• Cost per GB processed: < $0.10
• Cloud cost trend: Stable or decreasing
• Resource utilization: > 70%

Resources#

• Frameworks Guide: references/frameworks.md
• Code Templates: references/templates.md
• Tool Documentation: references/tools.md
• Python Scripts: scripts/ directory

---

Version: 2.0.0 Last Updated: December 16, 2025 Documentation Structure: Progressive disclosure with comprehensive references Streaming Enhancement: Task #8 - Real-time streaming capabilities added