We are looking for an experienced Snowflake Data Engineer with 7+ years of experience in data engineering, data warehousing, and ETL/ELT development. The ideal candidate should have strong hands-on expertise in Snowflake, Python/PySpark, SQL, and DBT and be capable of designing, developing, and optimizing scalable data pipelines and data warehouse solutions.
Key Responsibilities
- Design, develop, and maintain scalable data pipelines using Snowflake, Python/PySpark, SQL, and DBT.
- Develop and optimize complex SQL queries, stored procedures, views, and data transformation logic.
- Build robust ETL/ELT pipelines for ingesting and transforming data from multiple sources into Snowflake.
- Develop data transformation models using DBT (Data Build Tool), including staging, intermediate, and analytical models.
- Implement data quality checks, validation, reconciliation, and monitoring processes.
- Optimize Snowflake queries, warehouse utilization, storage, and overall data-processing performance.
- Develop PySpark/Python scripts for large-scale data processing and transformation.
- Work with structured and semi-structured data such as JSON, XML, and CSV within Snowflake.
- Implement incremental data loads and efficiently handle large data volumes.
- Follow best practices for data modeling, including fact/dimension tables, star schema, and dimensional modeling.
- Collaborate with data architects, analysts, developers, and business stakeholders to understand data requirements.
- Troubleshoot data pipeline failures, performance issues, and data inconsistencies.
- Participate in code reviews and ensure development follows engineering and data governance standards.
- Maintain technical documentation related to data pipelines, transformations, and data models.
Required Skills
- 7+ years of experience in Data Engineering or a similar role.
- Strong hands-on experience with Snowflake.
- Strong proficiency in SQL, including complex queries, CTEs, window functions, joins, and query optimization.
- Strong programming experience in Python and/or PySpark.
- Hands-on experience with DBT (Data Build Tool) and DBT-based data transformation.
- Strong understanding of ETL/ELT concepts and data pipeline architecture.
- Experience with data warehouse concepts and dimensional data modeling.
- Experience working with large datasets and optimizing data processing workloads.
- Good understanding of data quality, validation, and reconciliation techniques.
- Strong problem-solving and analytical skills.
