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Tier-1 brand, mid-level generalist title, metro location, and broad skillset increase applicant competition.
Core data engineering skills (SQL, Python, Airflow, dbt) are highly transferable across industries.
Mandatory 4-8 years plus required Snowflake, dbt, SQL, Python, and orchestration enforces strict candidate filters.
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Develop, maintain, and optimize scalable data pipelines for batch and streaming workloads supporting analytics and operations.
Build reliable data models, transformations, and integrations with new data sources, ensuring data quality and monitoring.
Collaborate with engineers, analysts, and data scientists to deliver well-governed data solutions and support DataOps processes including CI/CD and automation.
4-8 years of experience as a Data Engineer or similar role.
Mandatory hands-on experience with Snowflake including SQL, modeling, and optimization.
Proficiency with SQL and Python for data modeling, pipeline development, and automation.
Experience with orchestration tools (Airflow, Dagster, Prefect or equivalent) and familiarity with cloud environments (AWS, GCP, or Azure).
Experienced professional skilled in Snowflake platform and modern data warehouse/pipeline architecture best practices.
Strong practitioner of data engineering workflows including CI/CD, testing, and automation within DataOps framework.
Able to work in hybrid setup and collaborate effectively with cross-functional technical teams (engineers, analysts, data scientists).