Tech Trainer

• Experience Requirement: 6–12 Years
  
• Employment Type: Full-Time
  

• Classroom Instruction & Learning Delivery
  

• Technical Lecture & Lab Facilitation: Lead advanced, hands-on instructional cohorts for heterogeneous batches, adapting delivery formats seamlessly for fresh engineering graduates up to seasoned IT professionals.
  
• Databricks & Lakehouse Architectures: Deliver deep-dive training on Apache Spark, PySpark, Databricks Workspaces, Delta Lake, and Unity Catalog. Teach cohorts how to build scalable, optimized batch and streaming data pipelines within a lakehouse framework.
  
• Cloud Data Architecture: Instruct on end-to-end data pipelines using Microsoft Azure (Data Factory, Synapse Analytics), Google Cloud Platform (BigQuery, Compute Engine, GKE), and AWS foundational data services.
  
• Data Warehousing & ETL Frameworks: Deliver comprehensive curricula on modern cloud data warehouses like Snowflake alongside legacy ETL middleware systems such as Informatica PowerCenter.
  
• RDBMS & Production DBA Operations: Teach deep-dive database concepts across Oracle (11g/12c/19c), Microsoft SQL Server, MySQL, and PostgreSQL. Provide training on performance tuning, SQL/PL-SQL execution structures (stored procedures, packages, triggers), RMAN backup/recovery, and Automatic Storage Management (ASM).
  
• Data Operations & Automation: Train cohorts on core operational automation workflows, including Unix/Linux basics, shell scripting, job scheduling mechanisms, logging, error handling, and incident reporting tools (e.g., ServiceNow).
  

• Curriculum Architecture & Content Creation
  

• Build, evaluate, and maintain rigorous technical syllabi, instructional decks, and sandbox lab architectures modeled after industry standards and certification metrics (such as Databricks Certified Data Engineer Associate/Professional, Azure DP-203, and Oracle Certified Associate paths).
  
• Translate real-world application frameworks (e.g., multi-tier system architectures, real-time streaming data, or legacy database migrations) into clear, digestible, project-based training modules.
  

• Performance Management & Training Governance
  

• Measure, grade, and monitor cohort technical performance using metrics-based scorecards to ensure training programs translate directly into client-project and production readiness.
  
• Coordinate closely with delivery managers and corporate stakeholders to map specific technical skill gaps and continuously adapt material to evolving project pipelines.
  

• Education: Bachelor’s Degree in Engineering, Computer Science, Information Technology, or a related field.
  
• Experience: 6–12 years of overall professional experience combining corporate training, technical education, data engineering, or senior database administration roles.
  
  

• Databricks & Spark Ecosystem: Strong hands-on proficiency with Databricks, Apache Spark (PySpark/Spark SQL), Delta Lake ACID transactions, data optimization techniques (Z-Ordering, caching), and Lakehouse governance (Unity Catalog).
  
• Database Systems & Languages: Advanced mastery of SQL and PL-SQL. Proficient with Oracle DB engines, MySQL, SQL Server, and PostgreSQL. Strong understanding of query optimization and database tuning.
  
• Cloud Ecosystems: Strong conceptual and hands-on familiarity with Microsoft Azure Data tracks (ADF, ADLS Gen2), Google Cloud infrastructure (BigQuery), and basic AWS resources.
  
• Data Warehousing & ETL: Practical knowledge of Snowflake or Google BigQuery analytics alongside legacy Informatica PowerCenter workflow patterns.
  
• Programming & Automation: Strong proficiency in Python (especially for data manipulation via Pandas/PySpark). Familiarity with Core Java basics or C, paired with a solid understanding of Windows and Unix/Linux shell scripting environments.
  

• Databricks Certified Data Engineer Associate / Professional
  
• Microsoft Certified: Azure Data Engineer Associate (DP-203)
  
• Oracle Certified Professional (OCP) / Oracle Certified Associate (OCA)