Results-driven Data Engineer with 4+ years of specialized experience building scalable data infrastructure and robust ETL pipelines. Proven expertise in Python, SQL, Apache Airflow, and major cloud platforms, with successful track record improving data processing efficiency by 15% and ensuring 99.5%+ data accuracy for enterprise and U.S.-based projects.
I'm a results-driven Data Engineer with 4+ years of specialized experience in building scalable data infrastructure, robust ETL pipelines, and optimized cloud data warehouses. My journey began with a BSc in Data Science and Engineering from Kaunas University of Technology (KTU) in Lithuania, where I focused on data engineering solutions and parallel computing.
Throughout my career, I've worked on enterprise projects from Lithuania to Chicago, IL, successfully architecting systems that improved data processing efficiency by 15% and ensured over 99.5% data accuracy. I combine technical expertise in Python, SQL, Apache Airflow, and cloud platforms (AWS, GCP, Azure) with strong DevOps practices using Docker, Kubernetes, and Terraform.
My experience spans from building real-time streaming capabilities with Apache Kafka to migrating legacy Hadoop clusters to cloud-native solutions. I've also led successful freelance projects, delivering full-stack data solutions that eliminated manual processes and empowered clients with self-service analytics capabilities.
Architected full-stack data solution automating financial reporting and inventory management. Built end-to-end ETL pipeline extracting daily sales from Shopify/PayPal APIs, eliminating 20+ hours of monthly manual Excel work with automated report-ready data by 9 AM daily.
Led migration of 50+ GB operational data from legacy SQL Server to modern PostgreSQL with zero downtime. Optimized customer analytics dashboard performance, reducing report generation time from 2 minutes to under 15 seconds through query refactoring.
Engineered enterprise-scale data pipelines supporting analytics and BI for Chicago-based project. Reduced query costs by 15% through optimization, implemented real-time streaming with Kafka, and achieved 99.5%+ data accuracy through automated quality frameworks.
Delivered reliable data infrastructure for 5+ clients across e-commerce, marketing, and professional services. Built reusable Python framework for automated data quality checks, maintaining >99% data accuracy and proactively identifying discrepancies.
I'm always interested in discussing data engineering challenges, new opportunities, or potential collaborations. Feel free to reach out!