Data Engineer - Azure

at  Cognizant Technology Solutions

AT #COGNIZANT, WE HAVE AN IDEAL OPPORTUNITY FOR YOU TO BE PART OF ONE OF THE LARGEST COMPANIES IN THE DIGITAL INDUSTRY WORLDWIDE. A COMPANY WHERE WE LOOK FOR PEOPLE WHO CONTRIBUTE NEW IDEAS, EXPERIENCING A DYNAMIC AND GROWING ENVIRONMENT. AT COGNIZANT, WE PROMOTE AN INCLUSIVE CULTURE WHERE WE VALUE DIFFERENT PERSPECTIVES.

We are currently looking for a Data Engineering – Azure to work for an assurance, tax, strategy and consulting services project.

NECESSARY SKILLS AND EXPERIENCE REQUIRED:

• Bachelor’s degree in computer science, Data Science, Statistics, or a similar quantitative field.
• Language Level: Advanced English
• Strong proficiency in Python programming and experience with relevant frameworks (Pandas, NumPy, Dask)
• Experience with Azure
• Solid understanding of ETL processes, data modeling, and database management
• Experience in deploying and optimizing machine learning models in a production environment.
• Leadership and mentoring skills with a track record of managing and leading teams.
• Excellent problem-solving and communication

WHAT YOU´LL DO

As Data Engineering - Azure you will work closely with our internal customers and managing product operation. The ideal candidate should have a deep understanding of Azure.

KEY RESPONSIBILITIES:

• Collaborate with cross-functional teams to understand business requirements and align data engineering initiatives with organizational goals.
• Design, develop, and maintain scalable and efficient data pipelines using Python frameworks (e.g., Pandas, NumPy, Dask) for data processing and transformation. Expert in file processing.
• Implement and optimize ETL (Extract, Transform, Load) processes to ensure timely and accurate data delivery.
• Integrate machine learning models into the data pipeline, ensuring seamless deployment and scalability.
• Collaborate with data scientists and analysts to implement and optimize ML algorithms for predictive modeling and data analysis.
• Establish and enforce data quality standards, ensuring the accuracy, consistency, and reliability of data across systems.
• Implement and maintain data governance policies to comply with industry regulations and internal standards.