Snowflake Data Engineer

Key Responsibilities

• Design, develop, and maintain scalable data pipelines using Snowflake, Python, and dbt.
• Build and optimize ETL/ELT workflows to ingest, transform, and process structured and semi-structured data from multiple sources.
• Develop efficient data models and transformation logic to support reporting, analytics, and downstream applications.
• Implement incremental data loading, change data capture, and automated data processing wherever applicable.
• Ensure data pipelines are reliable, maintainable, and aligned with business requirements.

• Develop and manage Snowflake databases, schemas, tables, views, and other database objects.
• Optimize Snowflake queries, warehouse configurations, and data processing workflows for performance and cost efficiency.
• Work with Snowflake features such as streams, tasks, stages, and Snowpipe to support data ingestion and automation.
• Implement appropriate data access controls, security practices, and governance standards.
• Troubleshoot data processing issues and resolve performance bottlenecks.

• Develop, maintain, and optimize dbt models to implement modular, reusable, and scalable data transformations.
• Apply best practices for data modeling, model dependencies, incremental processing, and project organization.
• Implement dbt tests, documentation, and data quality checks to ensure accuracy and consistency.
• Manage model versioning, deployment workflows, and dependencies across development and production environments.

• Develop Python scripts and utilities to automate data ingestion, validation, transformation, and monitoring.
• Build reusable components to improve data processing efficiency and reduce manual intervention.
• Integrate Python applications with Snowflake and other data platforms or APIs.

• Collaborate with cross-functional teams to understand data requirements and translate them into technical solutions.
• Participate in code reviews, technical design discussions, testing, and production deployments.
• Monitor pipeline performance, troubleshoot production issues, and continuously improve data engineering practices.

Must-Have Skills

• Strong hands-on experience with Snowflake data warehousing and cloud data engineering.
• Practical experience with dbt for data transformation, modeling, testing, and documentation.
• Proficiency in Python for data processing, scripting, and workflow automation.
• Strong understanding of ETL/ELT concepts, data warehousing principles, and scalable data pipeline architecture.
• Experience with performance optimization, data quality management, and production troubleshooting.

Good-to-Have Skills

• Strong SQL skills, including complex queries, joins, CTEs, and query optimization.
• Experience with PySpark and distributed data processing.
• Familiarity with cloud platforms such as AWS, Azure, or GCP.
• Knowledge of orchestration tools, CI/CD pipelines, Git, and data governance practices.

Preferred Candidate Profile

• 5–10 years of relevant experience in data engineering, with substantial hands-on exposure to Snowflake, dbt, and Python.
• Strong analytical and problem-solving abilities.
• Excellent communication and collaboration skills.
• Ability to work independently, manage priorities, and deliver high-quality data solutions in a fast-paced environment.