Data Engineer

Key Responsibilities: • Design, develop, and maintain data pipelines leveraging Python, Spark/PySpark, and cloud-native services. • Build and optimize data workflows, ETL processes, and transformations for large-scale structured and semi-structured datasets. • Write advanced and efficient SQL queries against Snowflake, including joins, window functions, and performance tuning. • Develop backend and automation tools using Golang and/or Python as needed. • Implement scalable, secure, and high-quality data solutions across AWS servies such as S3, Lambda, Glue, Step Functions, EMR, and CloudWatch. • Troubleshoot complex production data issues, including pipeline failures, data quality gaps, and cloud environment challenges. • Perform root-cause analysis and implement automation to prevent recurring issues. • Collaborate with data scientists, analysts, platform engineers, and product teams to enable reliable, high-quality data access. • Ensure compliance with enterprise governance, data quality, and cloud security standards. • Participate in Agile ceremonies, code reviews, and DevOps practices to ensure high engineering quality. Key Requirements and Technology Experience: • Skills-Data Engineer- Python , Spark/PySpark, AWS, Golang, Able to write complex SQL queries against Snowflake tables / Troubleshoot issues, Java/Python, AWS (Glue, EC2, Lambda). • Proficiency in Python with experience building scalable data pipelines or ETL processes. • Strong hands-on experience with Spark/PySpark for distributed data processing. • Experience writing complex SQL queries (Snowflake preferred), including optimization and performance tuning. • Working knowledge of AWS cloud services used in data engineering (S3, Glue, Lambda, EMR, Step Functions, CloudWatch, IAM). • Experience with Golang for scripting, backend services, or performance-critical processes. • Strong debugging, troubleshooting, and analytical skills across cloud and data ecosystems. • Familiarity with CI/CD workflows, Git, and automated testing.