Sr. Azure Data Engineer

Location : Princeton NJ

Long Term

Primary Skill: Azure Databricks, Python, Azure Data Factory*****

Skillset and Experience

• Overall 12+ years in IT, with 4+ years of skills with Data Migration and Azure Cloud Data Migration and Data Warehousing related projects.
  
• Experience in developing and maintaining modern ingestion pipeline using technologies like (Azure Data Factory, SSIS, Spark etc)
  
• Experience with Insurance Domain
  
• Strong experience in design and configuration of data movement and transformation (ETL) technologies such as Azure Data Factory
  
• Hands on experience on Azure Cloud and its Native components like Azure Data bricks
  
• Strong experience in Data storage technologies such as Azure SQL DW, Azure Data Lake
  
• Strong experience in analytics solutions using Databricks, Azure Synapse Analytics
  
• String experience in coding with languages like SQL, Pl/SQL
  
• Good-to-have Azure certification
  
• Good experience in Requirements gathering, Design & Development
  
• Working with cross-functional teams to meet strategic goals.
  
• Experience in high volume data environments
  
• Critical thinking and excellent verbal and written communication skills
  
• Strong problem solving and analytical abilities, should be able to work and delivery individually
  
• Good knowledge of data warehousing concepts
  
• Good communication skills
  
• Worked in agile methodologies and waterfall execution models
  
• Must have excellent oral and written communication with need to interact clients directly
  
• Must be both individual contributor, good team player and self-motivated to take on challenges
  

Roles and Responsibilities

• Provide technical and development support to client to build and maintain data pipeline
  
• Develop data mapping documents listing business and transformational rules
  
• Develop, unit test, deploy and maintain data pipelines
  
• Analyze source specifications and build data mapping documents
  
• Identify and document applicable non-functional requirements
  
• Understand profiling results and validate data quality rules
  
• Utilize data analysis tools to construct and manipulate datasets to support analyses
  
• Collaborate with and support Quality Assurance (QA) in building functional scenarios and validating results