Overview
0.0
Contract - W2
Skills
3-5+ years of experience working with Azure Data Lake.
Job Details
Title: Azure Data Engineer
Location: Remote w/ 1 Monthly on-site meeting to Philadelphia, PA required (University City Section)
Must reside in Pennsylvania, New Jersey or Delaware only (for benefits purposes once converted)
Type of Employment: Contact to Hire (6 Months to Hire)
Responsibilities:
Design, develop, and maintain scalable data pipelines and architectures on Azure Cloud.
Implement and manage Azure Data Lake solutions to store and process large volumes of data.
Utilize Databricks for data processing, transformation, and analytics.
Develop and maintain ETL processes using Azure Data Factory.
Write efficient and optimized SQL queries to extract, transform, and load data.
Work with relational databases to ensure data integrity and performance.
Collaborate with data scientists, analysts, and other stakeholders to understand data requirements and deliver solutions.
Monitor and troubleshoot data pipelines to ensure reliability and performance.
Implement data security and compliance measures in accordance with industry standards.
Qualifications:
Bachelors degree in Computer Science, Information Technology, or a related field is required.
8-10+ years of Data Engineering experience with a focus on Azure Cloud services for 2-3+ years.
3-5+ years of experience working with Azure Data Lake.
3-5+ years of Python for data processing and automation.
2+ years of Databricks experience.
1-2+ years of Azure Data Factory (ADF) for extra, transfer, load process.
5+ years of SQL and prior experience with other ETL processes such as SSIS.
Familiarity with data governance and security best practices.
Excellent problem-solving skills and attention to detail.
Strong communication and collaboration abilities.
Preferred Qualifications:
Masters degree in a related field.
Certification in Azure Data Engineering or related Azure certifications.
Experience with other Azure services such as Azure Synapse Analytics, Azure Machine Learning, etc.
Knowledge of big data technologies like Hadoop, Spark, etc.
Location: Remote w/ 1 Monthly on-site meeting to Philadelphia, PA required (University City Section)
Must reside in Pennsylvania, New Jersey or Delaware only (for benefits purposes once converted)
Type of Employment: Contact to Hire (6 Months to Hire)
Responsibilities:
Design, develop, and maintain scalable data pipelines and architectures on Azure Cloud.
Implement and manage Azure Data Lake solutions to store and process large volumes of data.
Utilize Databricks for data processing, transformation, and analytics.
Develop and maintain ETL processes using Azure Data Factory.
Write efficient and optimized SQL queries to extract, transform, and load data.
Work with relational databases to ensure data integrity and performance.
Collaborate with data scientists, analysts, and other stakeholders to understand data requirements and deliver solutions.
Monitor and troubleshoot data pipelines to ensure reliability and performance.
Implement data security and compliance measures in accordance with industry standards.
Qualifications:
Bachelors degree in Computer Science, Information Technology, or a related field is required.
8-10+ years of Data Engineering experience with a focus on Azure Cloud services for 2-3+ years.
3-5+ years of experience working with Azure Data Lake.
3-5+ years of Python for data processing and automation.
2+ years of Databricks experience.
1-2+ years of Azure Data Factory (ADF) for extra, transfer, load process.
5+ years of SQL and prior experience with other ETL processes such as SSIS.
Familiarity with data governance and security best practices.
Excellent problem-solving skills and attention to detail.
Strong communication and collaboration abilities.
Preferred Qualifications:
Masters degree in a related field.
Certification in Azure Data Engineering or related Azure certifications.
Experience with other Azure services such as Azure Synapse Analytics, Azure Machine Learning, etc.
Knowledge of big data technologies like Hadoop, Spark, etc.
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