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Protocol Intelligence
Data-driven signals on your job's competitivenessTier-1 brand, generalist title, and metro locations increase applicant density despite some niche HPCC skills.
Core data engineering skills transfer well, but HPCC/ECL and insurance domain knowledge increase specificity.
Multiple mandatory technologies (Azure, Databricks, PySpark, HPCC/ECL) raise technical filtering and strictness.
Job Description
Structured overview of role & requirementsAbout This Role
Design, develop, and optimize scalable ETL/ELT data pipelines using Azure Data Factory, Databricks, and Azure storage services.
Ingest and transform large-scale structured and semi-structured data from multiple sources including relational databases and APIs into Azure data platforms.
Collaborate with analysts, data scientists, and application teams to deliver high-quality data solutions supporting analytics, reporting, and operations.
Minimum Requirements
Strong proficiency in SQL; solid understanding of data warehousing and ETL/ELT patterns.
Hands-on experience with Azure Databricks, Spark SQL, PySpark, Azure Data Factory, ADLS Gen2, and Azure SQL/Synapse.
Experience working with large-scale structured and semi-structured datasets.
Work Experience Required: Not explicitly mentioned in the JD.
Ideal Candidate Profile
Experienced in designing and optimizing data pipelines using modern Azure-native tools in enterprise-scale environments.
Familiar with multiple data processing languages and platforms including ECL on HPCC, PySpark, Spark SQL, and Delta Lake.
Able to work cross-functionally with data scientists, analysts, and business teams to translate complex data requirements into efficient engineering solutions.