Snowflake Data Engineer (Airflow)

Snowflake Data Engineer (Airflow)

Responsibilities

• Develop and maintain data pipelines using Apache Airflow and Python.
• Build data ingestion processes from APIs, databases, and file-based sources into AWS S3 and Snowflake.
• Design and implement data transformations using dbt.
• Optimize Snowflake queries, warehouse performance, and resource consumption.
• Implement pipeline monitoring, error handling, and alerting mechanisms.
• Ensure data quality, consistency, and reliability through automated testing and validation.
• Maintain technical documentation and follow data engineering best practices.

Requirements

• 3+ years of professional experience in Data Engineering.
• Strong expertise in Apache Airflow, including DAG development, operators, sensors, hooks, TaskFlow API, and workflow optimization.
• Advanced Python skills, including writing clean, maintainable, and testable code.
• Advanced SQL knowledge, including complex queries, CTEs, window functions, and performance optimization.
• Hands-on experience with Snowflake, including warehouses, stages, external volumes, and data loading processes.
• Practical knowledge of dbt, including models, tests, macros, snapshots, and incremental transformations.
• Experience with AWS S3 and Data Lake architecture, including data partitioning, file formats, and access management.
• Familiarity with Git and CI/CD practices.
• English proficiency at B2 level or higher.

Nice to Have

• Experience with Docker and Kubernetes.
• Knowledge of Apache Iceberg or Delta Lake.
• Familiarity with data quality and observability tools such as Great Expectations or Soda.
• Understanding of dimensional data modeling (Kimball methodology).