About The Role

We are looking for a skilled and motivated Snowflake Data Engineer to join our data engineering team. The ideal candidate will bring hands-on experience with Snowflake, Iceberg and DBT, and play a key role in building and optimizing scalable, high-performance data pipelines. Exposure to modern ELT tools such as Fivetran is a strong plus.

This role is ideal for professionals who thrive in a fast-paced environment, are passionate about data, and are eager to contribute to the development of a robust data infrastructure.

Key Responsibilities

• Design, develop, and maintain scalable and efficient data pipelines using Snowflake and DBT
• Build and manage data models and transformations that support business intelligence and analytics needs
• Collaborate with data analysts, scientists, and stakeholders to gather requirements and deliver clean, well-modeled datasets
• Optimize performance and ensure reliability and scalability of the data infrastructure
• Implement best practices in data quality, testing, and monitoring
• Participate in code reviews and contribute to continuous improvement of the data engineering process
• Document data workflows, transformation logic, and system configurations
  
  

Must-Have Skills & Qualifications

• 10 - 12 years of hands-on experience in data engineering or a similar role
• Proficient in Snowflake architecture, development, and performance tuning
• Strong experience in DBT (Data Build Tool) for managing transformations and version-controlled data models
• Strong SQL skills with the ability to write optimized, complex queries for large datasets
• Experience working with structured and semi-structured data (e.g., JSON, Parquet)
• Understanding of data warehousing concepts, dimensional modeling, and ELT workflows
• Familiarity with cloud platforms (AWS, Azure, or GCP)
  
  

Good-to-Have Skills

• Experience with Fivetran or similar ELT tools for automated data ingestion
• Knowledge of orchestration tools like Airflow or dbt Cloud
• Familiarity with Git-based workflows and CI/CD practices in data engineering
• Exposure to data governance, security, and privacy frameworks
• Scripting skills (e.g., Python or Bash) for automation and tooling