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Mid-level metro role at a strong analytics firm with a 3–5 year band increases applicant competition.
Azure Databricks and PySpark requirements make transitions possible but favor cloud-native data engineering backgrounds.
Multiple mandatory Azure/Databricks/PySpark/DevOps requirements and explicit 3–5 years make screening strict.
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Own design, development, and maintenance of data infrastructure, data pipelines, and database schemas to support business objectives.
Ensure data quality and integrity by developing and maintaining data pipelines for processing and analysis.
Collaborate with cross-functional teams to identify and prioritize data requirements and optimize distributed systems and storage solutions.
3-5 years of relevant work experience as a Data Engineer or similar role.
Proficiency in Azure services including Azure Data Factory (ADF), Azure Data Lake Storage Gen2 (ADLS2), and Databricks.
Strong programming skills in Python and PySpark with experience in DevOps practices.
Experience with P&G frameworks such as AI Factory, Ultimate, and Pygentic.
Experienced in managing end-to-end data engineering workflows within Azure cloud environments.
Skilled at implementing best practices and standards for data engineering in a collaborative, cross-functional setup.
Familiar with distributed systems design, data storage optimization, and business-aligned data modeling.