Irdeto Technology Off Campus Hiring

Qualifications

Required Qualifications & Skills:-

• Enrolled in a bachelor’s or master’s degree program in Computer Science, Data Science, Information Technology, or a related field.
• Familiarity with programming languages like Python, Java, or Scala.
• Basic understanding of databases and SQL.
• Knowledge of data integration and ETL concepts.
• Experience with big data technologies is a plus.
• Analytical mindset and attention to detail.
• Strong problem-solving skills and the ability to work independently and in a team.
• Good communication skills and the ability to collaborate effectively with various stakeholders.

Job Description & Responsibilities:-

As a Data Engineer Intern, you will work closely with the data engineering team to support the development, maintenance, and optimization of data infrastructure and pipelines. Your primary focus will be on data integration, data transformation, and data quality, enabling the efficient and reliable flow of data across various systems.

• Data Integration: Collaborate with data engineering and data science teams to understand data requirements and source data from various internal and external systems. Implement data extraction, transformation, and loading (ETL) processes to ensure a smooth flow of data into the data warehouse.
• Data Pipeline Development: Assist in designing and building scalable, robust, and maintainable data pipelines that collect, process, and store data from different sources. Work with technologies like Apache Spark, Apache Kafka, or other relevant tools.
• Data Quality Assurance: Contribute to the development and implementation of data quality checks and validation procedures to ensure data accuracy, consistency, and completeness.
• Database Management: Help manage and maintain databases, ensuring data integrity, security, and high availability. Monitor database performance and troubleshoot issues as they arise.
• Documentation: Document data engineering processes, data pipelines, and system configurations to facilitate knowledge sharing and ensure that the data infrastructure remains well-documented.
• Automation: Identify opportunities to automate repetitive tasks and processes within data engineering workflows, enhancing efficiency and reducing manual efforts.
• Data Governance: Adhere to data governance and security policies, maintaining data privacy and confidentiality throughout all data-related activities.
• Collaborative Projects: Collaborate with cross-functional teams on data-related projects, supporting their data needs and contributing to the success of company initiatives.
• Continuous Learning: Stay updated with the latest advancements in data engineering technologies and practices, and proactively apply this knowledge to improve data engineering processes.