Trainer

• As a Trainer, you will Teach production-grade Data Engineering and Handle mixed learner depth - Break concepts down for freshers while retaining credibility with experienced engineers.
• Develop practitioners who can think, troubleshoot, explain and make sound engineering judgments.

Key Responsibilities : -

Curriculum & Content Development : -

• Architect endtoend training curriculam: slide decks, guided notebooks, quizzes, handson labs, and capstone projects
• Balance theory and practice with a 50/50 lecturetolab ratio
• Update materials iteratively based on learner feedback and platform enhancements

Training Delivery : -

• Lead engaging virtual and inperson sessions, live demos, and whiteboard discussions
• Facilitate selfpaced elearning modules and mentor learners through code reviews and project assessments
• Measure effectiveness via surveys, handson evaluations, and grading rubrics

Technical Enablement : -

• Teach core Databricks componentsApache Spark, Delta Lake, MLflow, Unity Catalog, Databricks SQL, Delta Live Tables, and Workflows
• Guide learners through realworld use cases: batch & streaming ETL, ML pipelines, data governance, and performance tuning

Project Coaching & Support : -

• Mentor capstone teams on endtoend implementations, from data ingestion through model deployment
• Troubleshoot learner environments and provide officehours support

Continuous Improvement & Thought Leadership : -

• Stay current on Databricks product roadmap and industry best practices
• Contribute to internal playbooks, cookbook recipes, and knowledgeshare sessions

Required Skills & Experience : -

Instructional Design & Facilitation : -

• 2+ years delivering technical training (virtual and classroom) with strong adultlearning methodology
• Skilled at creating interactive labs, assessments, and capstone challenges
• Databricks Lakehouse Expertise
• Deep proficiency in Apache Spark (PySpark RDD/DataFrame/Dataset) and Delta Lake (ACID, time travel, Zordering)
• Solid experience with MLflow (experiment tracking & model registry), Unity Catalog, Databricks SQL, and Photon performance tuning

Programming & Data Engineering:

• Advanced Python (UDFs, pandas/NumPy) and SQL (window functions, query optimization)
• Practical knowledge of Scala for Spark application tuning (optional but advantageous)
• Handson with batch (Auto Loader, COPYINTO) and streaming (Structured Streaming, watermarking) ETL patterns, following Medallion architecture

Machine Learning Foundations:

• Feature engineering, hyperparameter tuning, model packaging/deployment
• Exposure to advanced analytics (NLP, recommendation systems, realtime inference)

Cloud & DevOps:

• Experience provisioning and securing Databricks on AWS, Azure, or GCP (Terraform/ARM templates)
• Building CI/CD pipelines for notebook version control, workflow deployment, and model lifecycles

Data Governance & Security:

• Designing Unity Catalog hierarchies, enforcing row/columnlevel security, and managing audit logging
• Familiarity with GDPR/HIPAA compliance and encryption/keymanagement best practices

Consulting & Stakeholder Management:

• Clientfacing experience in presales or solutions architecture, aligning training to real business challenges
• Agile mindset: sprint planning, iterative demos, and feedback loops

Certifications : -

• Databricks Certified Data Engineer Associate or ML Associate (required)
• Cloud certifications (AWS Big Data Specialty, Azure Data Engineer, or GCP Professional Data Engineer)