Top 20 Azure Data Engineer jobs- New York City, NY Quick Apply

Data Engineers play a critical role in the design, development, and management of data architecture and infrastructure within an organization. Here are the top 20 job responsibilities of a Data Engineer:

1. Data Architecture Design:
   • Designing and implementing scalable and robust data architecture solutions.
2. Data Modeling:
   • Developing and maintaining data models for databases and data processing systems.
3. Data Integration:
   • Integrating data from various sources and formats into a unified and coherent system.
4. ETL (Extract, Transform, Load) Development:
   • Designing and implementing ETL processes to move and transform data between systems.
5. Data Pipeline Development:
   • Building and maintaining end-to-end data pipelines for efficient data processing.
6. Database Management:
   • Managing and optimizing databases, ensuring data consistency, integrity, and performance.
7. Big Data Technologies:
   • Working with big data technologies such as Hadoop, Spark, and other distributed computing frameworks.
8. Cloud Platforms:
   • Deploying and managing data solutions on cloud platforms like AWS, Azure, or Google Cloud.
9. Data Quality Assurance:
   • Implementing data quality checks and ensuring data accuracy and reliability.

1. Streaming Data Processing:
   • Developing solutions for processing real-time streaming data.
2. Data Warehousing:
   • Designing and maintaining data warehouse solutions for efficient data storage and retrieval.
3. Metadata Management:
   • Managing metadata to facilitate data discovery and understanding.
4. Version Control:
   • Utilizing version control systems for managing code and configurations.
5. Data Security:
   • Implementing security measures to protect sensitive data and ensure compliance.
6. Collaboration with Data Scientists:
   • Collaborating with data scientists to deploy machine learning models into production.
7. Scalability Planning:
   • Planning and implementing solutions that scale with the growing volume of data.
8. Data Cataloging:
   • Developing and maintaining a data catalog to document and organize available datasets.
9. Automation Scripting:
   • Developing automation scripts for monitoring, maintenance, and deployment tasks.
10. Performance Optimization:
    • Optimizing data processing and storage for improved performance.
11. Documentation:
    • Documenting data engineering processes, architecture, and configurations.

Data Engineers work at the intersection of data architecture, software engineering, and infrastructure management. They ensure that data is accessible, reliable, and ready for analysis by other data professionals within the organization.