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Medium due to strong employer and metro location balanced by seniority and specialized data engineering requirements.
Medium because technical data engineering skills transfer widely, though healthcare domain preference increases bias.
High due to explicit 10+ years, leadership requirement, and mandatory Snowflake/Databricks/Azure skills.
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Design and build scalable, high-performance data platforms and pipelines using Snowflake, Databricks, and Azure.
Lead technical architecture decisions and enforce data engineering standards across globally distributed teams.
Ensure data quality, security, and compliance while driving automation and operational excellence in enterprise data ecosystems.
Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or related field.
10+ years in Data Engineering with at least 3 years in technical leadership roles.
Strong proficiency in Snowflake, Databricks, Azure platforms, SQL, Python, and data modeling.
Experience leading globally distributed engineering teams.
Experienced in designing and optimizing cloud-native data platform architectures with emphasis on scalability, performance, and cost efficiency.
Proven ability to translate complex business requirements into robust data engineering solutions and influence technical strategy.
Demonstrated success in mentoring senior engineers and driving adoption of DevOps, DataOps, and automation frameworks in data engineering environments.