Automating repetitive data work
I use Python, Excel and API-based workflows to reduce manual effort across data preparation, validation, enrichment, reporting and operational processes.
Portfolio overview
With 5+ years of hands-on data experience across logistics, research and international
business environments, I build practical data workflows with Python, SQL, Power BI and
Excel automation - reducing manual work, improving data reliability and helping teams
make faster decisions.
My experience includes automating ERP data preparation with Python, building Power BI
dashboards for management reporting, integrating supplier APIs, cleaning research and
operational datasets, and developing internal tools that streamline request handling
and reporting workflows.
CV Overview
Download my CV in English or German for a structured overview of my experience, technical skills and education.
At a glance
I bring a practical, business-focused approach to data work: Python automation, Power BI reporting, data quality improvement and process understanding.
In practice, this means turning manual, time-consuming workflows into repeatable data processes that teams can rely on. A recent example is my work at DB Schenker, where my contribution was credited with reducing manual data preparation time by approximately 80%, shortening reporting turnaround from days to hours, and contributing to a 50–60% reduction in customer response time for parts requests.
I use Python, Excel and API-based workflows to reduce manual effort across data preparation, validation, enrichment, reporting and operational processes.
I create Power BI dashboards and KPI views for operational and management reporting, with a focus on clear structure, usable filters and business-friendly summaries.
I clean, validate, standardize and cross-check datasets using naming rules, format checks, duplicate checks and consistency logic so teams can trust the numbers they use.
I document workflows, explain outputs clearly and build tools around how colleagues actually work, not only around how the data should theoretically look.
Recommendation letters
My recommendation letters show a consistent pattern across different environments: fast understanding of complex processes, structured execution, practical problem-solving, clear documentation, reliability, and strong technical execution with Python, Excel, and Power BI.
Operational impact
“Significantly reduced manual data preparation time by approximately 80% and lowered data error rates through the successful automation of ERP data processing.”
Open letter →
Python advantage
“Sergei’s knowledge of Python has been a huge advantage to our team.”
Open letter →
Research data work
“His command of advanced data-management functions in MS Excel, Python and Stata was crucial for efficient and timely task execution.”
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Problem-solving
“With his quick comprehension and ability to solve problems promptly and creatively, he greatly enriched our committee.”
Open letter →
Explore
Use the sections below to review my background, dashboards, projects and credentials.