Snowflake Engineer

Required Skills & Experience
You should have between 5 to 8 years or 8 to 10 years of professional experience in data engineering or analytics engineering roles. Strong experience with Snowflake is essential, including building scalable data solutions, warehouse management, query tuning, and cost optimization. You must have deep hands-on experience with DBT (dbt Core or dbt Cloud), including incremental models, snapshots, seeds, and testing. Advanced proficiency in SQL for data transformation, analysis, and modeling is required. Familiarity with Python for orchestration, scripting, and basic automation is expected. A solid grasp of dimensional modelling, normalization, and best practices for analytics-ready data is critical. You should be proficient with Git-based workflows, including feature branching, pull requests, and code reviews. Experience with orchestration tools such as Airflow, Prefect, or Dagster is a plus. Hands-on experience with one or more cloud platforms (AWS, GCP, or Azure) and data services (e.g., S3, GCS, Azure Data Lake) is necessary. You should also have working knowledge of CI/CD pipelines for data projects using tools like dbt Cloud, GitHub Actions, GitLab CI, or CircleCI. It would be beneficial to have experience with data cataloguing and lineage tools such as Atlan, Amundsen, or Monte Carlo. Exposure to data observability platforms and testing frameworks like Great Expectations is a plus. Familiarity with Agile or Scrum development environments is advantageous. An understanding of data privacy and compliance standards, such as GDPR or HIPAA, would also be valuable.