The program focuses on practice: Python, SQL, ETL, data pipelines and Big Data platforms. Goal: structured data architecture and automated metric updates.
Language of course:
Ukrainian
Format of the event:
Virtual classrooms
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Course overview
Description generated based on course syllabus and open data.
The Data Engineering program emphasizes hands‑on data engineering: from essential Python and SQL to building and orchestrating ETL/ELT pipelines, automating data pipelines, and designing scalable Big Data platforms. Focus areas include metric correctness, reproducibility, and data quality.
Data Engineering: program overview
The curriculum covers data sources, transformations, loading into warehouses, and building a data platform for analytics. It includes orchestration tools, data quality controls, and pipeline monitoring.
Who it fits / who it does not fit in Data Engineering
Best suited for data and infrastructure roles
- Analysts, BI/DS specialists, and Python/SQL developers who need stable data pipelines and ETL.
- Backend/DevOps engineers integrating services with a data platform and automating metric refresh.
- Professionals organizing data architecture and ensuring solution scalability.
Not a fit
- Those expecting theory only without coding, SQL, and orchestration tools.
- Those not planning to work with infrastructure, data warehouses, or cloud services.
Problem → outcome in Data Engineering
Challenges and expected effects
- Ad‑hoc data architecture → standardized layers (raw/staging/data warehouse).
- Manual ETL → automated scheduling, repeatable jobs, and failure recovery.
- Fragmented sources → centralized data platform with catalog and lineage.
- Inconsistent metrics → unified business logic in SQL/Python, data quality tests, and versioning.
Comparison with Data Engineering alternatives
Self‑study
- Pros: flexibility and freedom of stack choice.
- Limits: fragmentation, risk of missing critical orchestration and monitoring patterns.
Academic programs
- Pros: strong fundamentals.
- Limits: less practice in production data pipelines and tool integration.
BI/ML‑oriented path
- Pros: focus on analytics/models.
- Limits: infrastructure topics (ETL, DWH, orchestration) often remain uncovered.
Outcomes of completing the Data Engineering program
Competencies and artifacts
- Designing data architecture and storage models (warehouse/lake, batch/stream).
- Developing ETL/ELT in Python/SQL with data pipeline orchestration.
- Setting up schedulers, monitoring, alerting, and data quality checks.
- Cataloging, documentation, and version control for data and schemas.
- Foundational cloud data platform practices and scalability.
Course Description
The course is for those who want to put their data architecture in order and master the key tools of a data engineer in practice. In this course, we will start with basic Python and SQL expressions that will help you find the correct data. Next, we will learn how to set up ETL processes and transfer data between systems, run automatic updates of data pipelines, and build scalable Big Data Platforms.As a result, you will master 6 key tools of a data engineer and communicate the right metrics to make effective business decisions.Lecturer -Mykhailo LazorykData Engineer at Grid Dynamicshas 6+ years of experience with Big Data and 2+ years of teaching experiencestarted his career as a Python developer at Ericssonhas experience working with global clients such as Jabilworked with large amounts of data, was responsible for building ETL processesimplemented solutions in marketing, logistics, and telecommunications
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