Job
Description
Role Description Job Overview: UST is seeking a skilled Snowflake Engineer with 5 to 7 years of experience to join our team. The ideal candidate will play a key role in the development, implementation, and optimization of data solutions on the Snowflake cloud platform. The candidate should have strong expertise in Snowflake , PySpark , and a solid understanding of ETL processes, along with proficiency in data engineering and data processing technologies. This role is essential for designing and maintaining high-performance data pipelines and data warehouses , focusing on scalability and efficient data storage, with a specific emphasis on transforming data using PySpark . Key Responsibilities Snowflake Data Warehouse Development: Design, implement, and optimize data warehouses on the Snowflake cloud platform. Ensure the effective utilization of Snowflake’s features for scalable, efficient, and high-performance data storage and processing. Data Pipeline Development: Develop, implement, and optimize end-to-end data pipelines on the Snowflake platform. Design and maintain ETL workflows to enable seamless data processing across systems. Data Transformation with PySpark: Leverage PySpark for data transformations within the Snowflake environment. Implement complex data cleansing, enrichment, and validation processes using PySpark to ensure the highest data quality. Collaboration: Work closely with cross-functional teams to design data solutions aligned with business requirements. Engage with stakeholders to understand business needs and translate them into technical solutions. Optimization: Continuously monitor and optimize data storage, processing, and retrieval performance in Snowflake. Leverage Snowflake’s capabilities for scalable data storage and data processing to ensure efficient performance. Required Qualifications Experience: 5 to 7 years of experience as a Data Engineer, with a strong emphasis on Snowflake. Proven experience in designing, implementing, and optimizing data warehouses on the Snowflake platform. Expertise in PySpark for data processing and analytics. Technical Skills: Snowflake: Strong knowledge of Snowflake architecture, features, and best practices for data storage and performance optimization. PySpark: Proficiency in PySpark for data transformation, cleansing, and processing within the Snowflake environment. ETL: Experience with ETL processes to extract, transform, and load data into Snowflake. Programming Languages: Proficiency in Python, SQL, or Scala for data processing and transformations. Data Modeling: Experience with data modeling techniques and designing efficient data schemas for optimal performance in Snowflake. Skills Snowflake,Pyspark,Sql,Etl Show more Show less
