Data Engineer - ETL/Python

Essential Duties & Responsibilities :

- Design, develop, and optimize scalable data pipelines for ETL/ELT processes.

- Develop and maintain Python-based data processing scripts and automation tools.

- Write and optimize complex SQL queries (preferably in Snowflake) for data transformation and analytics.

- Work with Jenkins or other CI/CD tools to automate workflows.

- Develop and maintain solutions using Snowflake as the primary data platform.

- Work with ETL/ELT tools (preferably Fivetran, dbt) to manage data workflows.

- Implement version control best practices using Git or other tools to manage code changes.

- Collaborate with cross-functional teams (analysts, product managers, and engineers) to translate business needs into technical data solutions.

- Ensure data integrity, security, and governance across multiple data sources.

- Optimize query performance and database architecture for efficiency and scalability.

- Lead troubleshooting and debugging efforts for data-related issues.

- Document data workflows, architectures, and best practices to ensure maintainability and knowledge sharing.

Required Experience & Skills :

- 7+ years of experience in Data Engineering, Software Engineering, or a related field.

- High proficiency in SQL (preferably Snowflake) for data modeling, performance tuning, and optimization.

- Strong expertise in Python for data processing and automation.

- Experience with Git or other version control tools in a collaborative development environment.

- Strong communication skills and ability to collaborate with cross-functional teams for requirements gathering and solution design.

- Experience working with large-scale, distributed data systems and cloud data warehouses.

Preferred Experience & Skills :

- Experience developing Flask-based web applications to create lightweight data-driven tools.

- Familiarity with data governance, security, and compliance best practices.

- Understanding of data modeling principles and database design.

- Experience with containerization (Docker, Kubernetes) and CI/CD pipelines for data engineering

workflows.