Data Engineer

Key Responsibilities:

• Design and develop ETL/ELT pipelines that extract, transform, and load data from multiple sources including databases, APIs, and external systems.
• Collaborate with data scientists and analysts to optimize data quality, governance, and security for AI/ML models.
• Perform data validation, cleaning, aggregation, filtering, and enrichment to ensure accuracy and consistency.
• Optimize data pipelines for performance, scalability, and efficiency, leveraging AI tools like GitHub Co-Pilot where applicable.
• Monitor and tune data systems; identify and resolve performance bottlenecks; implement caching and indexing strategies.
• Implement data quality checks and validations within pipelines; perform Data QA post-integration to support QA teams.
• Establish governance for data and analytics used in decision-making, automation, and AI/ML augmentation.
• Collaborate with business leaders to treat data as a strategic enterprise asset.
• Design and implement modern data architectures using cloud services (AWS, Azure) and modern warehouses (Snowflake, Databricks).
• Support cross-functional teams and educate end-users on data products and analytics capabilities.