Data Engineering Trainer

Job highlights

• Teaching Experience: Prior experience in teaching or training,especially in a freelance or online setting,is highly desirable

• Experience: Minimum of 2-5 years of professional experience in data engineering,with hands-on experience in building and managing data pipelines,ETL processes,and data warehousing

• Course Development: Design and develop comprehensive training programs in data engineering, tailored to various skill levels, including beginner, intermediate, and advanced learners. Topics include data pipeline development, ETL processes, data warehousing, and big data technologies.

• Content Creation: Create and update instructional materials such as presentations, coding exercises, datasets, and real-world case studies that reflect current data engineering practices and technologies.

• Training Delivery: Conduct live, interactive training sessions via online platforms, focusing on essential data engineering skills such as data modeling, pipeline automation, data integration, and database management.

• Student Assessment: Evaluate student performance through quizzes, assignments, and practical projects. Provide detailed feedback to support their growth and understanding of data engineering concepts.

• Support and Mentorship: Offer personalized support to students, addressing their questions and helping them troubleshoot issues related to data engineering tools, techniques, and methodologies.

• Continuous Improvement: Stay updated with the latest trends and advancements in data engineering, including new tools, technologies, and best practices, and incorporate these advancements into the training curriculum.

Qualifications:

For Experienced Professionals:

• Experience: Minimum of 2-5 years of professional experience in data engineering, with hands-on experience in building and managing data pipelines, ETL processes, and data warehousing.
• Teaching Experience: Prior experience in teaching or training, especially in a freelance or online setting, is highly desirable.
• Technical Skills: Proficiency in data engineering tools and technologies such as Apache Hadoop, Apache Spark, Airflow, and database management systems (e.g., SQL, NoSQL). Familiarity with cloud-based data services (e.g., AWS Redshift, Google BigQuery, Azure Synapse) is a plus.
• Certifications: Relevant certifications in data engineering or related fields (e.g., Google Cloud Professional Data Engineer, AWS Certified Data Analytics Specialty) are preferred but not mandatory.
• Communication Skills: Excellent communication skills with the ability to clearly and effectively explain complex data engineering concepts and techniques.

For Freshers:

• Education: A degree or certification in Data Engineering, Data Science, Computer Science, Engineering, or a related field.
• Technical Skills: Basic knowledge of data engineering concepts and tools, gained through academic coursework, internships, or personal projects. Familiarity with data engineering tools such as Apache Hadoop, Spark, or SQL is a plus.
• Passion for Teaching: A strong interest in teaching and mentoring students in data engineering, with a commitment to developing effective training methods.
• Communication Skills: Strong verbal and written communication skills, capable of making complex data engineering topics understandable to diverse audiences.