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Job Description
Structured overview of role & requirementsAbout This Role
Design, build, test, and maintain cloud-native ELT data pipelines into Snowflake and S3 Iceberg layers with scalable, modular architecture supporting incremental loads and reprocessing.
Develop and maintain data transformations using dbt and Python, including automation, monitoring, and anomaly handling to ensure high data quality and pipeline reliability.
Author, schedule, and monitor workflows using Apache Airflow; implement CI/CD pipelines with Git and GitHub Actions for automated testing, deployment, and version control integration.
Minimum Requirements
3-5 years of professional experience in Data Engineering or related technical roles including internships.
Bachelor's degree in Computer Science, IT, Engineering, or related quantitative field.
Proficiency in Python, experience with Snowflake data warehouse, and familiarity with dbt and Apache Airflow.
Experience with Git version control and CI/CD pipelines, preferably GitHub Actions; comfort using AI-assisted coding tools in engineering workflows.
Ideal Candidate Profile
Practitioner comfortable with cloud-native ELT pipeline development and automation using modern data stack technologies (Snowflake, dbt, Airflow).
Experience applying CI/CD best practices and automation for testing and deployment in data engineering contexts.
Open to leveraging AI tools for development and documentation to enhance productivity and maintain modern engineering workflows.