Snowflake & DBT Data Engineer

You will be part of a dynamic data engineering team and play a crucial role in designing and developing scalable data pipelines using Snowflake and dbt. Your responsibilities will include: - Designing, developing, and optimizing data pipelines using dbt and Snowflake. - Building efficient, reliable, and scalable data transformation models with dbt Core or dbt Cloud. - Implementing Snowflake features such as Snowpipe, Streams, Tasks, and Dynamic Tables. - Collaborating with Data Analysts, Analytics Engineers, and Business teams to understand data requirements. - Ensuring data quality through rigorous testing and validation using dbt tests. - Maintaining and enhancing the data warehouse architecture to support business intelligence needs. - Monitoring data pipeline performance and proactively troubleshooting issues. - Applying version control practices (Git) and CI/CD for data workflows. - Demonstrating strong proficiency in Python for writing production-grade code, interacting with APIs, and automating workflows. - Handling large-scale data ingestion, transformation, and processing tasks to ensure data quality, reliability, and scalability. Qualifications required for this role include: - 5+ years of experience in Data Engineering. - Strong hands-on experience with Snowflake, including data modeling, performance tuning, and administration. - Advanced proficiency in dbt (Core or Cloud) for data transformations and testing. - Proficiency in SQL for complex queries, CTEs, window functions, and optimization. - Experience with ETL/ELT design patterns and tools like Apache Nifi, Airflow, and Fivetran. - Solid understanding of data warehousing concepts, dimensional modeling, and medallion architecture. - Experience with AWS Cloud Platforms is a must, or experience with other cloud service providers like Azure or GCP is a plus. - Familiarity with Git/GitHub and version-controlled deployment pipelines. - Excellent communication skills and ability to work effectively in cross-functional teams. - Demonstrated ability to thrive in fast-paced environments, with a strong aptitude for diving deep into datasets and identifying data quality issues.,