Cloud Data Engineering Trainer

Job Description: We are looking for a skilled and passionate Cloud Data Engineering Trainer to deliver in-depth training on cloud data engineering concepts, tools, and best practices. This role is perfect for both experienced professionals and freshers who are enthusiastic about teaching cloud data engineering and guiding students in mastering data pipelines, data storage, and data processing in cloud environments. As a freelance trainer, you will create and deliver engaging courses that focus on designing, building, and maintaining scalable data infrastructure on cloud platforms.

Key Responsibilities:

• Course Development: Design and develop comprehensive training programs on cloud data engineering tailored to various skill levels, including beginners, intermediate, and advanced learners. Topics include data pipeline design, data integration, ETL processes, data lakes, and data warehousing.
• Content Creation: Develop and regularly update training materials such as presentations, hands-on labs, case studies, and real-world projects using cloud platforms like AWS, Microsoft Azure, and Google Cloud.
• Training Delivery: Conduct interactive and engaging training sessions both online and in-person, focusing on key areas such as data ingestion, storage solutions, data processing with Spark, and cloud-native data engineering tools.
• Student Assessment: Evaluate student performance through quizzes, hands-on assignments, and data engineering projects. Provide constructive feedback to help students enhance their skills in cloud data engineering.
• Support and Mentorship: Offer personalized support and mentorship to students, assisting them with troubleshooting data pipeline issues, optimizing data workflows, and implementing best practices in cloud data engineering.
• Continuous Improvement: Stay updated with the latest trends, technologies, and tools in cloud data engineering. Regularly revise the training curriculum to incorporate new cloud services, data processing tools, and evolving best practices.

Qualifications:

For Experienced Professionals:

• Experience: Minimum of 3-5 years of professional experience in cloud data engineering, with hands-on experience in designing and managing data pipelines, ETL processes, and data lakes on AWS, Azure, or Google Cloud.
• Teaching Experience: Previous experience in teaching, training, or mentoring in cloud data engineering, big data, or data science is highly preferred.
• Technical Skills: Strong knowledge of data engineering tools and concepts including ETL, data warehousing, data lakes, cloud-native data processing services like AWS Glue, Azure Data Factory, Google Cloud Dataflow, Apache Spark, and Kafka.
• Certifications: Relevant certifications such as AWS Certified Data Analytics – Specialty, Microsoft Certified: Azure Data Engineer Associate, or Google Cloud Professional Data Engineer are preferred.
• Communication Skills: Excellent presentation and communication skills with the ability to simplify complex data engineering concepts and tools for learners.

For Freshers:

• Education: A degree or certification in Data Engineering, Cloud Computing, Computer Science, or related fields with a focus on data management and cloud technologies.
• Technical Skills: Basic understanding of cloud data engineering principles gained through coursework, internships, or personal projects. Familiarity with data processing and cloud services like AWS S3, Azure Blob Storage, or Google Cloud BigQuery is a plus.
• Passion for Teaching: A strong desire to teach and mentor students in cloud data engineering, with a focus on hands-on learning and practical applications.
• Communication Skills: Strong verbal and written skills, capable of conveying complex data engineering topics in an accessible manner.