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Metro-based Data Engineer title but specialized Snowflake/DBT certification reduces applicant pool.
Snowflake and DBT platform specificity raises domain bias, reducing transferability across unrelated industries.
Mandatory SnowPro certification plus specific Snowflake, DBT, Airflow, PySpark and release pipeline requirements enforce strict filtering.
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Lead design, development, and operation of complex data processing pipelines using modern data engineering tools.
Define and implement data governance, security policies, and data modeling techniques aligned with overall data architecture.
Collaborate with product managers and data intelligence teams to translate business requirements into scalable data solutions, and manage deployment through CI/CD practices.
Work Experience Required: Not explicitly mentioned in the JD
Must have SnowPro Core certification and hands-on experience with Snowflake.
Proficient in Python (especially PySpark), DBT, Airflow (deployment and operator integration), and cloud platforms AWS and Azure.
Experience with Azure DevOps ecosystem including design and managing build/release pipelines, and familiarity with Data Vault methodologies.
Technical leader capable of deep coding and mentoring junior engineers within data pipeline and cloud data warehousing environments.
Experienced in data governance, security implementation, and integration of DevOps practices across the data lifecycle.
Strong strategic thinker who can harmonize data engineering practices with business needs and data architecture to deliver production-ready scalable data models.