Data Engineer (AWS, Snowflake, PySpark, SQL) at Onsite ( Full Time )

• Design, develop, and maintain scalable ETL/ELT data pipelines.
• Build and optimize data processing solutions using PySpark and Snowflake.
• Develop cloud-native data solutions leveraging AWS services.
• Create and maintain data models for analytics and reporting requirements.
• Perform data ingestion from multiple structured and unstructured data sources.
• Optimize SQL queries, data pipelines, and Snowflake performance.
• Ensure data quality, governance, security, and compliance standards.
• Collaborate with Data Architects, Business Analysts, and Data Scientists.
• Troubleshoot production issues and provide ongoing support.
• Implement monitoring, logging, and performance tuning strategies.
• Participate in code reviews and follow engineering best practices.
• Document technical solutions, processes, and workflows.

We are seeking an experienced Data Engineer with strong expertise in AWS, Snowflake, PySpark, and SQL to design, develop, and optimize scalable data pipelines and cloud-based data solutions. The ideal candidate will have hands-on experience building modern data platforms, processing large datasets, and supporting analytics and reporting requirements. Must Have Technical/Functional Skills

• 5+ years of experience in Data Engineering.
• Strong hands-on experience with AWS Cloud Services (S3, Glue, Lambda, EMR, Redshift, etc.).
• Expertise in Snowflake Data Warehouse development and administration.
• Strong experience in PySpark/Spark for large-scale data processing.
• Advanced SQL skills with query optimization and performance tuning.
• Experience in building and maintaining ETL/ELT pipelines.
• Strong understanding of Data Warehousing and Data Lake concepts.
• Experience with data modeling, partitioning, and optimization techniques.
• Knowledge of version control tools such as Git.
• Experience working in Agile/Scrum environments.