Responsibilities:

• 5+ years of progressive experience in data engineering or a similar leadership role.
• Expert-level proficiency in Python and extensive hands-on experience with PySpark for building and optimizing large-scale data processing workflows.
• Deep understanding and significant practical experience with Snowflake as a cloud data warehouse, including advanced data loading techniques, complex query optimization, and performance tuning.
• Proven expertise in utilizing DBT (Data Build Tool) for advanced data transformation, modeling, and implementing data governance within the data warehouse.
• Extensive experience architecting and implementing data pipelines on Azure Databricks, including optimizing Spark configurations and leveraging Databricks features.
• Strong familiarity with a wide range of Azure cloud services, particularly those related to data (e.g., Azure Data Factory, Azure Blob Storage, Azure Synapse Analytics).
• Comprehensive understanding of advanced data warehousing concepts, sophisticated ETL/ELT methodologies, and dimensional modeling principles, including best practices for schema design and data partitioning.
• Mastery of SQL with extensive experience in writing complex queries, performance tuning, and working with very large datasets.
• Demonstrated experience in establishing and implementing robust data quality and data governance frameworks and tools.
• Ability to write clean, well-documented, and highly efficient production-ready code.
• Exceptional problem-solving and analytical skills with the ability to tackle complex data challenges independently.
• Excellent communication, presentation, and interpersonal skills with the ability to effectively communicate technical concepts to both technical and non-technical audiences.
• Proven ability to work independently, lead initiatives, and collaborate effectively within a team environment.