Full Stack Azure Data Engineer-11910-Remote

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Requirements:

• Strong hands-on experience leading design thinking as well as the ability to translate ideas to clearly articulate technical solutions.
• Bachelor’s degree in computer science, information systems, or other technology-related field or equivalent number of years of experience.
• Advanced hands-on experience implementing and supporting large scale data processing pipelines and migrations using technologies (eg. Azure Services, Python programming)
• Significant hands-on experience guiding technical teams as a mentor while leading collaboration with multiple teams across the organization.
• Significant hands-on experience with Azure services such as Azure Data Factory, Azure Databricks, Azure Data Lake Storage (ADLS Gen2), Azure SQL, and other data sources.
• Significant hands-on experience designing and implementing reusable frameworks using Apache Spark (PySpark preferred or Java/Scala)
• Solid foundation in data structures, algorithms, design patterns and strong analytical and problem-solving skills.
• Experience with any of the following Analytics and Information Management competencies: Data Management and Architecture, Performance Management, Information Delivery and Advanced Analytics Posting Description – Digital Data Engineer

Responsibilities:

• Build end-to-end direct capabilities.
• Create and maintain optimal data pipeline architecture.
• Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources.
• Use analytics for capitalizing on the data for making decisions and achieving better outcomes for the business.
• Derive insights to differentiate member and team member experiences.
• Collaborate with cross-functional teams.
• Analyze and define with product teams the data migration and data integration strategies.
• Apply experience in analytics, data visualization and modeling to find solutions for a variety of business and technical problems.
• Querying and analyzing small and large data sets to discover patterns and deliver meaningful insights.
• Integrate source systems with information management solutions and target systems for automated migration processes.
• Create proof-of-concepts to demonstrate viability of solutions under consideration.