Hyderabad, IndiaFull TimeMid-levelPosted Today
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, modern 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
- 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)