DataTerminal Hiring Drive 2024: DataTerminal Hiring Drive 2024 for its Data Engineer. DataTerminal Drive organizes off-campus drives for candidates in any batch. To apply, candidates should have a Bachelor’s degree. if you are interested, please apply as soon as possible.
Company Name: DataTerminal
Role: Data Engineer
Location: Remote
Experience: 1-2 years
Qualification: Bachelor’s degree
Batch: Any
Job Type: Full Time
Salary: Best in Industry
DataTerminal Hiring Drive 2024
Job Description:
- We’re seeking a talented Data Engineer to be a pivotal part of our team at DataTerminal.
- If you’re passionate about data engineering, cloud platforms, and ETL processes, let’s connect!
Requirements:
- Data Warehousing: Expertise in designing and implementing data warehouses.
- ETL Processes: Proficiency in building and maintaining ETL pipelines.
- Data Pipeline Tools: Experience with Apache Airflow or similar tools.
- Cloud Platforms: Proficiency with AWS, Azure, and Google Cloud (GCP).
- Big Data Technologies: Hands-on experience with Hadoop, Spark, and other big data frameworks.
- Database Management: Strong skills in managing MySQL, PostgreSQL, and MongoDB.
- Programming Languages: Proficiency in Python and SQL for data manipulation and automation.
- DevOps Tools: Experience with Docker, Kubernetes, and CI/CD pipelines.
Responsibility:
- Data Warehousing:Design and implement efficient data warehouses for optimal data storage and retrieval.
- Develop scalable data models to support analytical and reporting requirements.
- ETL Processes:Build and maintain robust ETL pipelines to ensure seamless data flow.
- Extract, transform, and load data from various sources to target databases.
- Data Pipeline Development:Create and manage automated data pipelines for real-time and batch processing.
- Utilize tools like Apache Airflow to schedule and monitor data workflows.
- Cloud Data Engineering:Deploy and manage scalable data solutions on cloud platforms such as AWS, Azure, and Google Cloud (GCP).
- Optimize cloud resources for performance and cost-efficiency.
- Big Data Processing:Handle large-scale data processing using technologies like Hadoop and Spark.
- Implement solutions for distributed data processing and storage
- Database Management and optimize databases including MySQL, PostgreSQL, and MongoDB.
- Ensure data integrity, availability, and security across all database systems.
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