Sr. Azure Data Engineer 12+

Overview

On Site
Depends on Experience
Contract - Independent
Contract - W2
Contract - 12 Month(s)

Skills

Azure
Azure Data Factory

Job Details

Role: Sr. Azure Data Engineer 12+

Location: Princeton, NJ

Long Term

Job Description:

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

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