I am a cloud data engineer with over 5 years of experience in Data Engineering, currently specializing in building scalable data pipelines and cloud-native solutions. My technical expertise is centered on Python, SQL, and the PySpark ecosystem for large-scale data processing, complemented by extensive hands-on experience with the AWS ecosystem. I architect and implement serverless data solutions using services such as Glue, S3, DMS for database migrations, Lambda for compute, Athena for interactive querying, and RDS for relational storage.
In my current role at Plazo Technologies in Minsk, Belarus, I am leading a project to redesign and accelerate a critical credit application processing and scoring platform for a growing fintech startup. This system processes data in real time from multiple sources to calculate personalized credit scores, automate decisions, and monitor risk portfolios. My work focuses on reducing loan issuance time, improving scoring accuracy, and ensuring compliance with regulatory requirements such as KYC and antifraud.
Previously, I worked at Cox Automotive where I modernized the ETL/ELT ecosystem for a leading SaaS provider in the automotive industry. I consolidated data from hundreds of dealerships and external sources into a unified analytical platform, enabling business units to access reliable data for pricing, demand forecasting, inventory management, and customer engagement. I successfully reduced AWS costs by 67% while improving efficiency through pipeline redesign and optimization.
I have strong skills in optimizing ETL workflows using Apache Airflow, implementing CI/CD pipelines with GitLab, and developing monitoring algorithms to ensure high pipeline reliability. I am experienced in SQL query optimization, data integration from various systems, and configuring incremental DBT models to reduce memory usage. I also implement alerting systems to notify teams of failures and errors in data workflows.
My technical toolkit includes Python, SQL, Pandas, NumPy, Apache Spark, and various AWS services such as Glue, Athena, Lambda, and Kinesis. I am passionate about building scalable, efficient, and reliable data infrastructure that drives business value and supports data-driven decision-making.
Faculty of Information Technologies and Robotics (FITR)
Redesigned and accelerated a credit application processing and scoring platform for a fintech startup. Processed real-time data from multiple sources to calculate credit scores, automate decisions, and monitor risk portfolios. Optimized ETL workflows using Apache Airflow, implemented CI/CD pipelines with GitLab, developed monitoring algorithms, optimized SQL queries, integrated data from various systems, configured incremental DBT models, and implemented alerting systems for workflow failures.
Modernized ETL/ELT ecosystem for a SaaS automotive provider. Consolidated data from dealerships and external sources into a single analytical platform. Reduced AWS costs by 67%, configured AWS Kinesis for streaming data, implemented data validation and error handling, optimized Apache Spark jobs, managed AWS Glue data catalogs, migrated workflows to DBT, partnered with stakeholders to refine data models, and automated ETL workflows using AWS MWAA and DBT.
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