Data Engineer

• Design, develop, and maintain scalable ETL/ELT pipelines using Python, PySpark, and SQL.
• Perform data extraction, transformation, and loading from multiple source systems into enterprise data platforms.
• Develop reusable data transformation logic to support business reporting, analytics, and machine learning initiatives.
• Optimize SQL queries and PySpark jobs to improve performance and processing efficiency.
• Build and maintain data models, staging layers, and curated datasets for downstream consumption.
• Perform data cleansing, validation, reconciliation, and quality checks to ensure data accuracy and consistency.

Requirements

• Troubleshoot and resolve data pipeline failures, performance bottlenecks, and data-related issues.
• Collaborate with business analysts, data architects, and data scientists to understand data requirements and implement scalable solutions.
• Participate in code reviews, testing, deployment, and production support activities.
• Develop and maintain technical documentation, including data mappings, transformation logic, and ETL workflows.
• Ensure compliance with data governance, security, and regulatory standards.
• Monitor scheduled ETL jobs and proactively address operational issues.