Azure Data Engineer

4 weeks ago

Bengaluru Karnataka India, Karnataka Tata Consultancy Services Full time

Greetings from TCS


**TCS is Hiring for Azure Data Engineer**


Role: Azure Data Engineer


Desired Experience: 4- 8 Years


Job Location: Bengaluru


Job Description:


  • Data Pipeline Design and Development: Collaborate with data scientists, analysts, and other stakeholders to understand data requirements. Design, develop, and implement efficient data pipelines, integrating various data sources to transform raw data into usable formats.
  • Cloud Data Platform: Utilize your expertise in working with cloud-based data platforms, especially Azure, to architect, deploy, and maintain scalable and reliable data infrastructure.
  • Databricks Expertise: Demonstrate a strong understanding of Databricks and its capabilities, using it as a primary platform for big data processing, analytics, and machine learning.
  • Data Modelling: Create and optimize data models that support data analytics, reporting, and visualization needs. Ensure data integrity, accuracy, and consistency.
  • Performance Optimization: Continuously monitor and fine-tune data pipelines and processing workflows to enhance overall performance and efficiency, considering large-scale data sets.
  • ETL (Extract, Transform, Load) Processes: Implement ETL processes to extract data from diverse sources, transform it into suitable formats, and load it into the data warehouse or analytical systems.
  • Data Security and Compliance: Ensure data security and compliance with data privacy regulations throughout the data engineering process.
  • Collaboration: Work closely with cross-functional teams, including data scientists, analysts, and business stakeholders, to understand data requirements, share insights, and provide technical support.
  • Troubleshooting and Issue Resolution: Proactively identify data-related issues and provide timely resolutions to maintain smooth data operations.
  • Documentation: Maintain comprehensive documentation for data pipelines, architecture, and processes, making it easier for team members to understand and contribute to the data engineering initiatives.

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