Role Overview

We are seeking an experienced, highly articulate, and hands-on Technical Trainer to design, develop, and deliver robust training programs across our cloud data engineering, up-to-date lakehouse architectures, and database administration practices. In this role, you will lead technical upskilling initiatives for both fresh graduates and experienced professionals, converting complex operational architectures into structured, production-ready corporate

curriculam.

The ideal candidate possesses practical, real-world experience across multi-cloud ecosystems (Azure, GCP, AWS), data lakehouse architectures (Databricks), modern data warehousing systems (Snowflake, BigQuery), traditional relational databases (Oracle, MySQL, SQL Server), and core ETL/ELT methodologies.

- Experience Requirement: 6–12 Years

- Employment Type: Full-Time

Core Responsibilities:

- 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.
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> - 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.
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> - 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.
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> - Data Warehousing & ETL Frameworks: Deliver comprehensive curricula on modern cloud data warehouses like Snowflake alongside legacy ETL middleware systems such as Informatica PowerCenter.
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> - 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).
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> - 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).
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- 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).
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> - 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.
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- 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.
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> - Coordinate closely with delivery managers and corporate stakeholders to map specific technical skill gaps and continuously adapt material to evolving project pipelines.
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Technical Profile & Qualifications

Education & Experience

- 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.

Required Domain Knowledge

? Key Technical Focus Areas

- 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.

Preferred Certifications (A Big Plus)

- Databricks Certified Data Engineer Associate / Professional

- Microsoft Certified: Azure Data Engineer Associate (DP-203)

- Oracle Certified Professional (OCP) / Oracle Certified Associate (OCA)

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