Data Engineer

Skills: Strong background in data engineering or cloud data platform development with proven expertise in AWS Glue and Snowflake Strong proficiency in Python and PySpark, with hands-on experience building, tuning, and orchestrating large-scale ETL pipelines Solid grasp of data modeling, data quality, and governance principles supporting finance or payment transaction data Demonstrated analytical/problem solving skills Experience working for a global tea Ability to work collaboratively in a team environment Ability to work effectively with people at all levels in an organization Responsibilities: Design, develop, and maintain robust and scalable data pipelines using AWS Glue for ETL/ELT processes, data transformation, and data integration from various sources (, S, relational databases, APIs) Develop and manage AWS Glue jobs (Python/PySpark) for data ingestion, transformation, and loading into Snowflake. Utilize AWS Glue Data Catalog for metadata management and integration with other AWS services Implement and optimize data warehousing solutions in Snowflake, including schema design, table creation, data loading, performance tuning, and query optimization Ensure data quality, consistency, and accuracy throughout the data lifecycle, implementing data validation and cleansing routines Collaborate with product managers and engineering teams to understand data requirements and deliver solutions that meet business needs Monitor and troubleshoot data pipelines and Snowflake performance, identifying and resolving issues proactively Implement best practices for data governance, security, and compliance within AWS and Snowflake environments