We are seeking an experienced Lead Data Engineer with strong expertise in AWS Cloud, Python/PySpark, SQL, Snowflake, and DBT. The ideal candidate will design, build, and optimize scalable data pipelines and data architectures to support advanced analytics and business intelligence initiatives.
Design, develop, and maintain scalable data pipelines using AWS services such as Glue, S3, Lambda, and Redshift Build and optimize ETL/ELT workflows using Python and PySpark Develop robust data models and transformations using SQL, Snowflake, and DBT Lead the design of data warehousing solutions and ensure data quality, integrity, and governance Collaborate with data analysts, data scientists, and business stakeholders to understand data requirements Optimize performance and cost of cloud-based data solutions Implement best practices for data security, monitoring, and reliability Mentor junior data engineers and lead technical discussions
4–12 years of experience in data engineering or related field Strong hands-on experience with AWS services: Glue, S3, Lambda, Redshift Proficiency in Python and PySpark for data processing Advanced knowledge of SQL and experience with Snowflake Experience with DBT (Data Build Tool) for data transformation and modeling Solid understanding of data warehousing concepts and ETL/ELT frameworks Experience in handling large-scale structured and unstructured data Familiarity with CI/CD pipelines and version control (Git)
Mandatory Skills
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