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Remote posting and common Data Engineer title increase applicant density, but seniority and Azure specialization limit it.
Highly domain-specific Azure Databricks, PySpark and data architecture requirements mean low cross-industry transferability.
Explicit 15+ years requirement plus mandatory Azure/Databricks/PySpark skills and technical leadership make filters strict.
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Design and develop scalable, secure, cost-effective enterprise data platforms and architectures on Microsoft Azure.
Analyze, refactor, and modernize existing data products and pipelines while leading technical improvements and mentorship of junior developers.
Translate business requirements into end-to-end data solutions using Azure data services including Databricks, Data Lake, Data Factory, and Synapse.
15+ years total experience with 10+ years specifically in data engineering and analytics solutions.
Strong hands-on expertise with Azure data ecosystem including Azure Data Lake, Azure Data Factory, Azure Synapse, and Azure Databricks.
Proficient in PySpark, Python, SQL, ETL/ELT development, data pipeline optimization, and data platform performance tuning.
Experience with data warehousing, lakehouse architectures, cloud migration, and Agile/CI-CD development practices.
Senior-level individual contributor with deep technical leadership responsibilities, including mentoring and code reviews.
Experienced in designing end-to-end data architectures aligned to business objectives and scalable enterprise solutions on Azure.
Skilled at analyzing and improving existing large-scale data pipelines and leading technical modernization efforts.