Overview
On Site
$60 - $65
Accepts corp to corp applications
Contract - Independent
Contract - W2
Contract - 12 Month(s)
Able to Provide Sponsorship
Skills
Databricks
Azure Data Factory
Python
Job Details
Role: Azure Data Engineer
Location: NJ (Onsite)
Primary Skill: Azure Data Factory, Azure Data Lake, Azure SQL DW, Azure Databricks, Python
Skillset and Experience
- Overall, 3-5 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
- Analyse 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
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