Building Scalable Data Solutions
Data Engineering Consultant building efficient, scalable data solutions that grow with your business — whether you're a Seed-stage startup, a Series B scale-up, or an enterprise optimizing costs.
Technical Expertise
Business Solutions Transformation
Supporting business personnel in transforming their requirements into successful technical solutions.
Data Pipeline Development
Expert in building robust ETL/ELT pipelines and data warehousing solutions
Data Visualization
Creating insightful dashboards and analytical tools
Cloud & Infrastructure
Specialized in cloud-native architectures and serverless solutions
Python Development
Advanced Python development for data processing and automation
Big Data Solutions
Experience with large-scale data processing and analytics
AI and Automation
Leveraging AI technologies in automation workflows
Education
Master of Engineering (ME), Computer Science, Intelligent Systems
Warsaw University of Technology
October 2020 - January 2023
Master's Thesis
"A comparative study of tools for building ETL processes"
Scientific Publication
"Epidemiology-constrained Seating Plan Problem"
Key Domains
- Development of database structures and applications for OLTP and OLAP systems
- Oracle technology from a systems designer's point of view (DBMS Oracle, XDK, APEX, BI Publisher, PL/SQL Web Toolkit)
- The objectives and different methodologies of modeling (DWH methodologies, ERD, UML, relational, object-oriented, object-relational, XML, JSON)
- Mathematical modeling for process optimisation in strategic and operational business activities
- Reliable, scalable and maintainable IT Systems
Bachelor of Engineering (BE), Electronics, Electronics and Computer Engineering
Warsaw University of Technology
October 2016 - September 2020
Bachelor's Thesis
"Geolocation system with mobile application"
Leadership
Chairman of Scientific Club of Digital Systems "DEMAIN" (January 2018 - February 2020)
Licenses & Certifications
Deploy and Manage Cloud Environments
Issued Dec 2020
Machine Learning
Stanford University
Issued Oct 2019
Credential ID 4EC6G7UECXXD
Publications
Epidemiology-constrained Seating Plan Problem
Foundations of Computing and Decision Sciences · September 1, 2022
doi: 10.2478/fcds-2022-0013
The emergence of an infectious disease pandemic may result in the introduction of restrictions in the distance and number of employees, as was the case of COVID-19 in 2020/2021. In the face of fluctuating restrictions, the process of determining seating plans in office space requires repetitive execution of seat assignments, and manual planning becomes a time-consuming and error-prone task. In this paper, we introduce the Epidemiology-constrained Seating Plan problem (ESP), and we show that it, in general, belongs to the NP-complete class. However, due to some regularities in input data that could affect computational complexity for practical cases, we conduct experiments for generated test cases. For that reason, we developed a computational environment, including the test case generator, and we published generated benchmarking test cases. Our results show that the problem can be solved to optimality by CPLEX solver only for specific settings, even in regular cases. Therefore, there is a need for new algorithms that could optimize seating plans in more general cases.