Architect robust ETL pipelines for efficient data extraction, transformation, and loading into data warehouses.

Develop and implement ETL processes using tools like Informatica, DataStage, or cloud-based services such as AWS Glue.

Design and implement complex data transformation logic to ensure data quality and consistency.

Integrate various data sources, including relational databases (SQL Server, Oracle), NoSQL databases, and cloud storage.

Optimize ETL processes for performance, scalability, and reliability to handle large datasets efficiently.

Implement data quality checks and validation rules to ensure data accuracy and integrity throughout the ETL process.

Develop error handling mechanisms and monitoring systems to identify and resolve ETL process failures promptly.

Manage and maintain metadata related to ETL processes, data sources, and data transformations for better governance.

Implement and manage ETL solutions on cloud platforms like AWS, Azure, or Google Cloud, leveraging cloud-native services.

Collaborate with data engineers, data scientists, and business analysts to understand data requirements and document ETL processes.

Implement security measures to protect sensitive data during ETL processes and ensure compliance with data privacy regulations.

Design and develop ETL processes to extract data from various sources, transform it, and load it into data warehouses or data lakes.

Write complex SQL queries and scripts to perform data extraction, cleansing, and transformation tasks.

Develop and maintain data pipelines using ETL tools like Informatica, DataStage, or cloud-based services such as AWS Glue or Azure Data Factory.

Monitor ETL processes and data quality, troubleshoot issues, and implement solutions to ensure data accuracy and reliability.

Collaborate with data architects, data analysts, and business stakeholders to understand data requirements and design ETL solutions that meet business needs.

Optimize ETL processes for performance and scalability, ensuring efficient data processing and minimal downtime.

Create and maintain ETL documentation, including data flow diagrams, data dictionaries, and process descriptions.

Implement data governance policies and procedures to ensure data quality, security, and compliance with regulatory requirements.

Perform data profiling and analysis to identify data quality issues and develop strategies for data remediation.

Develop and maintain data models to support ETL processes and data warehousing initiatives.

Automate ETL processes using scripting languages like Python or shell scripting to improve efficiency and reduce manual effort.

Stay up-to-date with the latest ETL technologies and trends, and evaluate new tools and techniques for potential adoption.

Participate in code reviews and testing to ensure the quality and reliability of ETL code.

Provide support and training to end-users on ETL processes and data access.

Manage and maintain ETL infrastructure, including servers, databases, and ETL tools.