Олена

Олена Яцев
A motivated aspiring Data Engineer eager to apply knowledge in real-world projects. Experienced in
SQL, ETL processes, and Azure tools. I bring a disciplined, results-oriented mindset and strong
problem-solving skills, and I am excited to grow within a collaborative team environment.

Education and Qualifications

Personal Bachelor’s Degree in Software Engineering Sept 2022 - Jun 2026
Lviv Polytechnic National University, Lviv, Ukraine
Name Database Systems & Data Warehousing: Confident in SQL and relational databases.
Олена Яцев Experienced in data modeling, normalization, and designing efficient storage solutions for
Address structured data. Can query, organize, and manage large datasets effectively.
Львів, Україна Data Processing (ETL/ELT): Skilled in building and managing ETL/ELT pipelines, handling
Phone number extraction, transformation, and loading of data across multiple systems. Understands data flow
[open contact info](look above in the "contact info" section) and automation of pipelines for high‑volume datasets.
Email Cloud Computing: Hands‑on experience with Azure and foundational knowledge of AWS.
[open contact info](look above in the "contact info" section) Capable of deploying and managing cloud storage, compute resources, and end‑to‑end data
LinkedIn workflows.
[open contact info](look above in the "contact info" section) Software Development Lifecycle (SDLC): Knowledgeable in all stages of the SDLC, from
requirements analysis and system design to development, testing, and deployment.
Soft skills Machine Learning: Understanding of supervised and unsupervised learning, data preprocessing,
and model evaluation techniques.
Analytical thinking Data Structures & Algorithms: Able to apply core data structures and algorithms to solve
problems and optimize data processing tasks.
Critical thinking
Attention to detail Data Engineer Jul 2025 - Present
Adaptability and fast learning EPAM Systems, Lviv, Ukraine
Team collaboration Worked with large volumes of data, performing data processing, transformation, and validation
Built and analyzed data pipelines using ETL/ELT pipelines
Languages Gained hands‑on experience with Azure Data Factory and explored Microsoft Fabric for data
integration and processing
Worked with data across the full lifecycle: from ingestion and storage to transformation and basic
Ukrainian Native visualization
English Intermediate Applied SQL skills to query, manipulate, and analyze structured data
Collaborated on practical tasks simulating real‑world data engineering scenarios

Projects

Sales and Inventory Tracking System
Designed and implemented a comprehensive system to log, process, and visualize sales and
inventory data from multiple sources. Demonstrated strong skills in building scalable data pipelines
and turning raw data into actionable insights, ready for business decision‑making.
Technologies: Microsoft Fabric, Spark, Azure SQL Database, Power BI
Built a system for structured storage and logging of sales and inventory data
Developed end‑to‑end Spark pipelines to process and transform large datasets
Optimized data for visualization in Power BI dashboards
Automated workflows from ingestion to visualization, ensuring accuracy and reliability

School Management System
Developed a system to manage and store records of students, teachers, and classes in a relational
database. Focused on secure and efficient handling of user data.
Technologies: PostgreSQL, TypeScript, Clerk
Designed relational database schema for students, teachers, classes, and attendance
Built backend logic with TypeScript and implemented user authentication via Clerk
Ensured data integrity, efficient querying, and role‑based access control

Open Data ETL/ELT Pipeline
Built a robust ETL/ELT pipeline to process large open datasets, transform them, and store in a
structured format for analysis. Gained hands‑on experience managing high‑volume data and
automating workflows.
Technologies: Azure, Azure Data Factory, SQL
Developed ETL/ELT pipeline for large open datasets into Azure SQL Database
Configured staging tables and automated data processing for high‑volume datasets
Applied SQL for validation, cleaning, and transformation of data
Streamlined workflows from ingestion to storage for efficient data handling

Hard Skills

SQL & Databases: PostgreSQL, Azure SQL Database, SQL Server, relational and NoSQL
databases
Data Warehousing & ETL/ELT: Data modeling, transformation, pipelines, loading and managing
data flows, ensuring data quality
Cloud Computing: Azure (Data Factory, SQL Database, Key Vault, Microsoft Fabric), AWS
fundamentals
Data Analytics & Visualization: Power BI, Excel, data cleaning and preparation
Artificial Intelligence: Practical experience with AI tools, prompt engineering, and integrating AI
models for data processing and analysis
Programming: Python, C#
Algorithms & Data Structures: Efficient problem‑solving and optimization

Tools & Technologies

Data Platforms: Azure Data Factory, Microsoft Fabric, Databricks (Basic)
Clouds: Azure, AWS fundamentals
Databases & SQL Tools: Azure SQL Database PostgreSQL, SQL Server, pgAdmin, SSMS
Data Processing & Analytics: Spark, Power BI, Tableau, Excel
Programming & Development: TypeScript, C#, Git, API integration
ETL/ELT & Workflow Automation: Building and managing data pipelines, data ingestion,
transformation, validation, and ensuring data quality