Data Pipeline Development

• Design, build, and maintain scalable ETL/ELT pipelines to support analytics and reporting needs
• Develop automated data ingestion processes from various sources into the data warehouse
• Implement data transformation workflows that ensure data consistency and reliability
• Monitor and troubleshoot pipeline issues to maintain data availability and accuracy
• Build reusable pipeline components and frameworks for efficient development

Data Transformation and Modeling

• Implement modular transformations and testing frameworks using DBT (Data Build Tool)
• Design efficient database schemas optimized for analytical workloads
• Create and maintain data models that support business requirements
• Write complex SQL queries and optimize them for performance
• Work with Amazon Athena or similar query services for large-scale data analysis

Workflow Orchestration and Automation

• Build and schedule data workflows using Apache Airflow
• Create dependencies and manage task execution across complex data pipelines
• Implement error handling, retry logic, and alerting mechanisms
• Automate routine data operations and maintenance tasks
• Use Python for data processing, automation, and custom integrations

Data Quality and Governance

• Implement data validation rules and quality checks throughout pipelines
• Establish data lineage tracking and documentation practices
• Build data observability solutions to monitor pipeline health and data integrity
• Collaborate with stakeholders to define and enforce data governance standards
• Create and maintain documentation for data models, pipelines, and processes

Performance Optimization

• Tune data pipelines and queries for scale, performance, and reliability
• Identify and resolve bottlenecks in data processing workflows
• Optimize data storage and partitioning strategies
• Monitor resource utilization and implement cost optimization measures
• Conduct performance testing and implement improvements