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Tier-1 brand, mid-level generalist data role, metro location, and broad skill requirements increase candidate competition.
Data engineering skills (pipelines, AWS, Python) are broadly transferable across industries, so fit sensitivity is low.
Explicit 5–7.5 years requirement plus mandatory tech stack (AWS, Airflow, Python, Databricks) creates strict filters.
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Design, develop, and manage advanced data architectures and pipelines ensuring data quality and accessibility for business insights.
Implement data solutions across platforms including AWS, Kubernetes, Teradata, and Databricks while managing data privacy and compliance.
Serve as a technical resource optimizing data ingestion, processing frameworks, and storage solutions to support key business initiatives.
5 to 7.5 years of relevant work experience in data engineering or related fields.
Proficient in AWS, Airflow, Python, and data pipeline development.
Bachelor's degree preferred; relevant coursework or extensive professional experience also considered.
Must be willing to work nights, weekends, and variable schedules as needed.
Experienced in designing scalable data architectures with strong focus on data quality and compliance.
Capable of independent judgment and discretion on significant technical matters within a cross-functional team environment.
Skilled at selecting appropriate data storage platforms and managing complex data transformations across cloud and on-premise environments.