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Medium due to a common Data Engineer title, mid-level experience, and broad Azure+Snowflake skill requirements.
Low because Azure, Snowflake, SQL and Python data engineering skills are broadly transferable across industries.
High because explicit 2–3 years requirement plus mandatory Azure, Snowflake, SQL and Python skills.
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Design, develop, and maintain scalable ETL/ELT data pipelines primarily on Microsoft Azure and Snowflake platforms.
Build and optimize data models, warehouses, and lakes; develop automation scripts using SQL and Python.
Implement CI/CD pipelines using Azure DevOps and ensure data quality, security, and performance across cloud-based data solutions.
2–3+ years of experience in data engineering or related roles.
Strong hands-on expertise in Microsoft Azure data engineering services and Snowflake data warehousing.
Proficiency in SQL and Python programming for data transformation and automation.
Experience with Azure DevOps including version control and CI/CD deployment automation.
Experienced in building and optimizing scalable cloud-based data pipelines and data warehouse solutions.
Familiar with Agile/Scrum methodologies and able to collaborate effectively across teams including analysts and product managers.
Comfortable with automation, workflow orchestration (preferably Azure DevOps; Apache Airflow is a plus) and maintaining data governance standards.