We are looking for an experienced Data Engineer to join a dynamic team in an exciting long-term project with one of our key clients in the Pharmaceutical industry. This project focuses on building and optimizing modern data pipelines and ensuring seamless integration across cloud-based data platforms.

The ideal candidate will have strong experience in data engineering with Python, Snowflake, and Airflow, combined with a deep understanding of data integration, cloud infrastructure, and modern ETL processes.

Senior Data Engineer (Python, Snowflake, Airflow)

Your responsibilities

• Design, develop, and maintain data pipelines using Python, Airflow, and Snowflake.
• Collaborate with cross-functional teams to understand data requirements and build efficient solutions for data integration.
• Implement and optimize ETL/ ELT processes, ensuring high-quality data transformation and delivery.
• Work with cloud-based platforms (AWS, GCP, or Azure) to ensure seamless data storage and processing.
• Leverage DBT for data modeling and transformation tasks.
• Ensure continuous integration and delivery (CI/CD) pipelines are effectively supporting data workflows.
• Provide support for troubleshooting, optimizing, and maintaining existing data systems.
• Design and implement best practices for managing data architectures (including Data Vault, Kimball, SCD).

Our requirements

• Strong experience in Data Engineering and data pipeline development
• Proficiency in Python, particularly for data processing, and familiarity with libraries like pandas, pyarrow, and SQLAlchemy.
• Solid experience with Snowflake (data warehousing), including working with warehouses and queries at scale.
• Hands-on experience with Apache Airflow for workflow orchestration.
• Strong knowledge of ETL/ELT processes and best practices.
• Familiarity with DBT and its use in data transformation.
• Experience with cloud platforms, such as AWS, GCP, or Azure.
• Understanding of modern data architecture concepts, including Data Vault, Kimball, and Slowly Changing Dimensions (SCD).
• Experience working in CI/CD environments for data pipelines.
• Experience with machine learning (ML) or related technologies.
• Familiarity with containerization technologies such as Docker.
• Exposure to additional data tools like Kafka, Spark, or Hado