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Develop and maintain data pipelines using Apache Airflow and Python.
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Build data ingestion processes from APIs, databases, and file-based sources into AWS S3 and Snowflake.
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Design and implement data transformations using dbt.
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Optimize Snowflake queries, warehouse performance, and resource consumption.
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Implement pipeline monitoring, error handling, and alerting mechanisms.
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Ensure data quality, consistency, and reliability through automated testing and validation.
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Maintain technical documentation and follow data engineering best practices.
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3+ years of professional experience in Data Engineering.
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Strong expertise in Apache Airflow, including DAG development, operators, sensors, hooks, TaskFlow API, and workflow optimization.
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Advanced Python skills, including writing clean, maintainable, and testable code.
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Advanced SQL knowledge, including complex queries, CTEs, window functions, and performance optimization.
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Hands-on experience with Snowflake, including warehouses, stages, external volumes, and data loading processes.
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Practical knowledge of dbt, including models, tests, macros, snapshots, and incremental transformations.
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Experience with AWS S3 and Data Lake architecture, including data partitioning, file formats, and access management.
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Familiarity with Git and CI/CD practices.
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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).
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