Валентин

VALENTYN SHTUL
JUNIOR DATA ENGINEER (TRAINEE LEVEL)

PROFILE
Master’s graduate in Applied Mathematics seeking a Junior Data Engineer
position. I have hands-on experience building data pipelines, working with
structured datasets, and automating data collection and transformation
workflows using Python, SQL, dbt, and Airflow. With a strong analytical
CONTACT background and algorithmic thinking, I am motivated to continuously grow and
contribute to building reliable and scalable data solutions.
[відкрити контакти](див. вище в блоці «контактна інформація»)

[відкрити контакти](див. вище в блоці «контактна інформація») PET PROJECTS

Luts’k, Ukraine ENGLISH VOCABULARY DATA PIPELINE

valentin808 Implemented a multi-stage data pipeline using Python and Playwright
to collect and process vocabulary data from Cambridge Dictionary
Valentyn S. and DeepL. The project sequentially gathers word lists, definitions,
parts of speech, and IPA pronunciations from Cambridge Dictionary,
EDUCATION then retrieves translations from DeepL. All intermediate and final
results are stored in structured JSON files, enabling further data
processing and analysis.
2024 - 2025
LUTSK NATIONAL TECHNICAL
UNIVERSITY AUTOMOTIVE DATA PIPELINE
Master’s in Applied
Implemented an end-to-end containerized data pipeline for
Mathematics (with Honors)
automotive listings, including data ingestion, storage, transformation,
and orchestration. Developed a Python-based web scraper to extract
2020 - 2024 structured car listing data and stored raw results in PostgreSQL.
NATIONAL AVIATION UNIVERSITY Designed a layered data architecture (raw, staging, mart) and built
Bachelor of Applied modular transformation models using dbt, including analytical
Mathematics aggregations and data quality tests. Orchestrated the full workflow
(scraping → transformation → validation) using Apache Airflow and
SKILLS managed the infrastructure with Docker and Docker Compose to
ensure reproducibility and environment isolation.
Docker
PySpark ETL PIPELINE FOR JSONPLACEHOLDER API
Python (NumPy, Pandas, PyTorch) An ETL pipeline was implemented in Python using the pandas and
PostgreSQL sqlalchemy libraries. The pipeline receives data from the public
JSONPlaceholder API (endpoint /posts), cleans and transforms it
SQLite
(calculating the length of the title and the number of words in the
Machine Learning body), and uploads the processed data to an SQLite database. The
Deep Learning project also includes the execution of analytical SQL queries (e.g.,
average header length, number of posts per user) and the generation
Critical Thinking
of reports in CSV and JSON formats.
dbt
AirFlow
OLYMPIADS & COMPETITIONS
LANGUAGES Participant in the university Olympiad in Mathematics
English (Upper-Intermediate) Participant in the Physics Tournament for high school students (twice)