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Mid-level common Data Engineer role, metro location, and broad Azure/Python/Spark requirements drive high applicant competition.
Core Azure, Python, and Spark skills are highly transferable across industries, so background sensitivity is low.
Mandatory 6+ years, 2+ years leading, and specific Azure/Databricks/Azure DevOps skills create strict shortlisting filters.
Own end-to-end design, implementation and delivery of data engineering solutions for large scale use cases.
Build, optimize and automate data pipelines and data supply chains by ingesting, transforming, cleansing, and combining data from multiple sources.
Support creation of architecture deliverables (data, processing, testing) and ensure cloud infrastructure meets solution requirements with adherence to Data Ops principles.
Degree in Mathematics, Scientific, Computing, Engineering discipline or equivalent experience.
6+ years in Data Engineering roles with at least 2 years leading design and implementation of large scale data solutions.
Experience with Azure data management components including Azure SQL, ADLS, Cosmos.
Proficiency in Python for data munging and experience with Azure DevOps, Spark (Databricks).
Experienced in building custom data engineering solutions using MS Azure platforms (IaaS/PaaS/SaaS).
Skilled in working cross-functionally with architects, business analysts and data scientists to translate business problems into data solutions.
Familiarity with regulated industry environments and ETL/data integration tools such as Informatica, Datastage, SSIS is a plus but not mandatory.