Snowflake Data Engineer

Data Engineer

The ideal candidate will have expertise in the following areas:

• Snowflake, Snowpark: The candidate should have a deep understanding of Snowflake data warehousing platform and be proficient in using Snowpark for data processing and analytics.
• AWS services (Airflow): The candidate should have hands-on experience with AWS services, particularly Apache Airflow for orchestrating complex data workflows and pipelines.
• AWS services (Lambda): Proficiency in AWS Lambda for serverless computing and event-driven architecture is essential for this role.
• AWS services (Glue): The candidate should be well-versed in AWS Glue for ETL (Extract, Transform, Load) processes and data integration.
• Python: Strong programming skills in Python are required for developing data pipelines, data transformations, and automation tasks.
• DBT: Experience with DBT (Data Build Tool) for modeling data and creating data transformation pipelines is a plus.

Responsibilities:

• Design, develop, and maintain data pipelines and ETL processes using Snowflake, AWS services, Python, and DBT.
• Collaborate with data scientists and analysts to understand data requirements and implement solutions.
• Optimize data workflows for performance, scalability, and reliability.
• Troubleshoot and resolve data-related issues in a timely manner.
• Stay updated on the latest technologies and best practices in data engineering.

Qualifications:

• Bachelor's degree in Computer Science, Engineering, or related field.
• Proven experience in data engineering roles with a focus on Snowflake, AWS services, Python, and DBT.
• Strong analytical and problem-solving skills.
• Excellent communication and teamwork abilities.
• AWS certifications (e.g., AWS Certified Data Analytics - Specialty) are a plus.

If you meet the above requirements and are passionate about data engineering and analytics, we encourage you to apply for this exciting opportunity.