Cloud Migration Data Engineer

4 days ago

Shimoga, Karnataka, India beBeeDataEngineer Full time ₹ 12,00,000 - ₹ 15,00,000
Job Overview

We are seeking a seasoned Data Engineer to join our team. This individual will be responsible for designing, developing, and implementing complex data pipelines that enable the migration of on-premise data systems to the cloud.

The ideal candidate will have a strong background in data engineering, ETL development, and cloud migration projects. They will possess excellent analytical, problem-solving, and communication skills, as well as experience working with large-scale data sets.

Required Skills and Qualifications
  • Minimum of 10 years of experience in IT industry, with focus on data engineering and ETL development.
  • Extensive hands-on experience with Datastage for building and managing ETL jobs.
  • Proven experience in Cloud Migration projects, with strong understanding of challenges and best practices involved in moving data to the cloud.
  • In-depth knowledge and hands-on experience with Azure Data Factory (ADF) for building and orchestrating data workflows.
  • Strong proficiency in Databricks and its ecosystem, including notebooks, jobs, and Delta Lake.
  • Expertise in PySpark for developing data processing applications on distributed computing framework.
  • Solid understanding of cloud platforms, preferably Microsoft Azure.
  • Experience with SQL and database concepts.
Key Responsibilities
  1. Design, develop, and deploy robust and scalable data pipelines for migrating data from various on-premise sources to cloud platforms.
  2. Serve as subject matter expert in project's core technologies, including Datastage, Azure Data Factory (ADF), Databricks, and PySpark.
  3. Collaborate with architects and technical leads to design efficient and effective cloud migration strategies and data transformation solutions.
  4. Ensure delivery of high-quality, well-documented, and maintainable code. Participate in code reviews and provide constructive feedback to peers.
  5. Analyze and optimize performance of data pipelines and applications to ensure efficiency and scalability.
  6. Troubleshoot and resolve complex technical issues related to data migration, data quality, and system performance.

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