Junior Data Engineer Training Modules
This 3-month program provides beginner-to-intermediate level knowledge and practical skills in data engineering, equipping participants with a foundational preparation to work with real-world data pipelines and cloud environments. Learners will gain hands-on experience with the end-to-end process of processing, storing, transferring, and preparing data for analytics through practical case studies.
Program Structure (3 Months)
Month 1: Foundations
- Module 1. Data Engineering Fundamentals
- Module 2. SQL for Data Processing
- Module 3. Python for Data Processing
Month 2: Data Pipeline
- Module 4. Data Modeling
- Module 5. ETL/ELT Engineering
- Module 6. Data Tools
Month 3: Modern Data Stack
- Module 7. Cloud Data Engineering
- Module 8. DataOps & Best Practices
- Module 9. BI Integration
- Mini Project. Capstone Project
Start Date: Monday, May 11 Class Days: Monday, Wednesday Schedule: 18:00–20:30 Location: Zaisan, Sky Wing Office, 6th Floor, Room 601
Junior Data Engineer Training
Junior Data Engineer-ийн сургалтын төлбөр нийт 4,800,000₮ ба суудал баталгаажуулахад 20% буюу 960,000₮-ийг төлнө.
Learning Outcomes
Upon completing the program, graduates will have acquired the following skills:
✔ Collecting data from multiple sources, cleaning it, and designing data pipelines ✔ Understanding the differences between batch and near-real-time pipelines ✔ Planning, developing, and automating ETL/ELT processes ✔ Processing and optimizing data using SQL ✔ Designing data warehouse and data mart architectures ✔ Working with star schema, dimension, and fact models ✔ Implementing data quality checks and validation logic ✔ Writing data transformation and automation scripts using Python ✔ Ingesting data from multiple source types including APIs, files, and databases ✔ Gaining foundational skills to run data pipelines in cloud environments ✔ Working with storage, compute, and warehouse services ✔ Diagnosing data pipeline errors and reading logs ✔ Understanding the fundamentals of performance optimization ✔ Preparing analytics-ready datasets for BI tools ✔ Completing a mini project on a real-world case to gain near-production environment experience
Graduates will be fully prepared at the Junior Data Engineer level, capable of developing junior-level data pipelines, preparing data warehouses, and working in cloud-based data environments.
Successful graduates will receive an official certificate validating their knowledge and skills, and will be recommended to major domestic organizations.
Who is a Data Engineer?
A Data Engineer is a professional who builds and maintains the infrastructure for collecting, cleaning, integrating, storing, and transferring an organization's data. They focus on ensuring that data is reliable, well-structured, and ready for analysis. In other words, they are the people who build, maintain, and automate the "data roads" that sit behind analytics, AI, and reporting systems.
Demand for data engineers is exceptionally high and is expected to continue growing. Driven by the widespread adoption of AI, big data, and cloud technologies, job openings in this field have grown by nearly 50% in recent years. Data engineering is projected to be one of the fastest-growing careers between 2025 and 2030, with organizations prioritizing the establishment of foundational data infrastructure. Demand continues to rise due to the growing need to process unstructured data and manage cloud platforms such as AWS and Azure.
Demand Trends (2025–2026)
Rapid growth: Demand for data engineers is growing faster than almost any other role in the field.
Impact of AI: As AI agents become widely deployed, demand has increased for specialized data engineers capable of processing unstructured data, building ingestion pipelines, and working with vector databases.
Salary: Growing faster than comparable professions, with average salaries typically exceeding $150,000.
Market Overview
The global big data and data engineering services market is projected to exceed $106 billion by 2025, making skilled professionals increasingly valuable. As long as organizations continue generating data, the need for professionals who can structure and process it will remain critical.
