Data Engineer - Chicago

apartmentRobert Half placeChicago calendar_month 

We are searching for a Data Engineer with skills in Fabric/Azure, Python, and cloud technologies to join our team. This role is located in Chicago, Illinois and offers a short-term contract employment opportunity. As a Data Engineer, you will be tasked with developing data pipelines in Fabric, understanding the Fabric medallion architecture, and utilizing Python for various projects.

Responsibilities:

  • Develop and manage data pipelines in Fabric, ensuring smooth deployment from development to production.
  • Gain a comprehensive understanding of Fabric medallion architecture, including bronze, silver, and gold layers.
  • Utilize your Python knowledge to enhance and optimize data processing tasks.
  • Utilize your skills in Apache Kafka, Apache Pig, and Apache Spark for data processing and analytics.
  • Use your knowledge of cloud technologies to manage and maintain the data infrastructure.
  • Leverage your skills in data visualization and algorithm implementation to create insightful data reports and visualizations.
  • Work with AWS technologies and API development to enhance data processing capabilities.
  • Maintain excellent communication skills, and be able to work remotely and independently under tight deadlines.
  • Use your knowledge of Apache Hadoop and analytics to analyze large data sets and derive insights.
  • Apply your skills in Microsoft Fabric and data pipelines to enhance data processing and storage capabilities. • Proficiency in Apache Kafka, Apache Pig, and Apache Spark is expected.
  • Familiarity with various Cloud Technologies is required.
  • Ability to work with Data Visualization tools and techniques.
  • Should have experience in Algorithm Implementation.
  • Strong background in Analytics is essential.
  • Experience with Apache Hadoop is a plus.
  • Proficiency in API Development.
  • Knowledge of AWS Technologies is required.
  • Familiarity with Microsoft Fabric is beneficial.
  • Experience in creating and managing Data Pipelines is crucial.
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