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Hybrid/remote flexibility, popular mid-level Data Engineer title, and common skillset drive high candidate competition.
Core skills (Python, SQL, AWS, Databricks) are highly transferable across industries.
Explicit multi-year requirements plus mandatory Databricks, Python, AWS, and SQL enable strict filtering.
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Design, develop, and implement data products and pipelines for batch and real-time processing to ensure accurate, secure, and accessible data.
Translate business data requirements into technical solutions including data models, quality rules, and user access methods for analytical and operational use.
Maintain and monitor data quality and continuously improve data integration processes using agile and DevSecOps practices.
5-8 years of experience in data management and software development, including 2+ years with Databricks and Python.
3+ years of Cloud technology experience, preferably AWS.
Strong SQL skills and experience with data warehousing, integration, and cleansing.
Experience with production issue resolution and software development lifecycle; Agile/Scrum experience required.
Experienced in full lifecycle data engineering including design, testing, deployment, and maintenance of data solutions in agile environments.
Capable of closely collaborating with business users and technology teams to translate complex data needs into technical implementations.
Comfortable working with large datasets, multiple data types (structured and unstructured), and cloud-based tools with an emphasis on security and data quality.