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Metro mid-level Data Engineer title is common but Snowflake specialization moderates applicant density.
Skills are transferable across industries but require specific Snowflake and cloud data engineering experience.
Explicit 4–9 years plus mandatory Snowflake, Python, SQL, cloud and CI/CD requirements increase shortlisting rigidity.
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Design, develop, and optimize large-scale data pipelines and transformation workflows on Snowflake and cloud platforms.
Develop and fine-tune Python scripts for data automation, validation, and integration to support enterprise analytics and reporting.
Collaborate with data engineers, analytics teams, and business stakeholders to deliver scalable, secure, and efficient data architectures aligned with organizational goals.
4 to 9 years of hands-on experience in data engineering with strong expertise in Snowflake and Python.
Proficient in SQL and PL/SQL for data querying, transformation, and performance tuning.
Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Technology, or related fields.
Experience deploying and managing data pipelines and solutions on cloud platforms (AWS, Azure, or GCP).
Experienced in building automated and scalable data workflows with a focus on performance optimization in enterprise environments.
Competent in using DevOps practices including Git, CI/CD pipelines, Terraform, and containerization tools like Docker for infrastructure automation.
Skilled at collaborating cross-functionally with technical and business teams to understand requirements and ensure data solutions meet operational and compliance standards.