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Remote role, popular data-engineer title, and broad Azure/Databricks skillset create moderate applicant density.
Core data engineering skills (Spark, ETL, cloud) are broadly transferable across industries, lowering background sensitivity.
Explicit 10+ years plus mandatory Azure Databricks, Spark, Delta Lake, and streaming expertise enforce strict filters.
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Develop and maintain scalable data pipelines and enhance data models using Azure cloud services including Databricks and ADF.
Develop best practices, reusable code, and frameworks for cloud-based data warehousing and ETL/ELT processes.
Drive data quality, accessibility, and data-driven decision-making across the organization by aligning data engineering solutions with business objectives.
Minimum 10 years of practical data engineering experience in enterprise settings.
Strong proficiency with Azure services, including Azure Databricks, Azure Functions, and Azure Data Factory.
Advanced skills in Apache Spark (PySpark), Python, Databricks SQL, and data pipeline development including orchestration/scheduling.
Work Experience Required: Minimum 10 years of relevant enterprise data engineering experience.
Experienced in cloud-native data architecture, data modeling (normalized, dimensional, Lakehouse) and distributed systems design.
Familiar with CI/CD tools (Azure DevOps, Git), Infrastructure as Code (Terraform, ARM templates), and data governance including Unity Catalog.
Candidates with exposure to insurance or healthcare data domains and relevant certifications (Databricks, Azure) have a competitive edge.