Data Engineer Trainer

BL Consultants · India

One of our MNC Client is Hiring Data Engineer 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 end‑to‑end training curriculam: slide decks, guided notebooks, quizzes, hands‑on labs, and capstone projects

Balance theory and practice with a 50/50 lecture‑to‑lab ratio

Update materials iteratively based on learner feedback and platform enhancements

Training Delivery

Lead engaging virtual and in‑person sessions, live demos, and whiteboard discussions

Facilitate self‑paced e‑learning modules and mentor learners through code reviews and project assessments

Measure effectiveness via surveys, hands‑on 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 real‑world use cases: batch & streaming ETL, ML pipelines, data governance, and performance tuning

Project Coaching & Support

Mentor capstone teams on end‑to‑end implementations, from data ingestion through model deployment

Troubleshoot learner environments and provide “office‑hours” support

Continuous Improvement & Thought Leadership

Stay current on Databricks product roadmap and industry best practices

Contribute to internal playbooks, cookbook recipes, and knowledge‑share sessions

Required Skills & Experience

Instructional Design & Facilitation

2+ years delivering technical training (virtual and classroom) with strong adult‑learning 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, Z‑ordering)

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)

Hands‑on with batch (Auto Loader, COPY INTO) 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, real‑time 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/column‑level security, and managing audit logging

Familiarity with GDPR/HIPAA compliance and encryption/key‑management best practices

Consulting & Stakeholder Management:

Client‑facing experience in pre‑sales or solutions architecture, aligning training to real business challenges

Agile mindset: sprint planning, iterative demos, and feedback loops

Preferred Qualifications

Certifications

Databricks Certified Data Engineer Associate or ML Associate (required)

Cloud certifications (AWS Big Data Specialty, Azure Data Engineer, or GCP Professional Data Engineer)