High school · Estonia
Abitur and academic preparation
Completed secondary education in Estonia before moving to Germany for university studies.
About & Experience
Background
Economics foundation, international experience and a practical data mindset.
My background across Estonia, Netherlands and Germany has made me adaptable, multilingual and comfortable in international environments. Studying and working across different education systems and team cultures helped me communicate clearly, learn quickly and stay flexible in unfamiliar situations.
Professionally, I moved from research data preparation at IAMO into corporate analytics at FedEx and later into automation, reporting and data quality work at DB Schenker. Across these roles, I learned how often business teams depend on data processes that are manual, scattered or difficult to trust - and how much value can be created when those processes are made more structured, automated and reliable.
That is what motivates me most in data work: creating practical solutions that make information easier to trust, processes easier to repeat, and outputs easier for business users to act on.
Vertical timeline
How my education and work experience connect.
The timeline below gives a clearer explanation of what I did at each stage and how it shaped my current focus on analytics, automation and data quality.
Technical University of Applied Sciences Würzburg-Schweinfurt · Germany
International Business Logistics, B.Eng. program - completed two semesters
Completed two semesters of International Business Logistics before switching to Business Economics, which better matched my interest in business processes, analysis and decision support.
Martin Luther University Halle-Wittenberg · Germany
B.Sc. Business Economics
My studies in Business Economics helped me understand how organizations think about decisions, processes, performance, and efficiency. This became the business foundation behind my later focus on analytics and automation.
AIESEC · Volunteering · Germany
Team Leader, Global Outgoing Exchange Team
Alongside my university studies, I volunteered with AIESEC in Halle as Team Leader of the Global Outgoing Exchange team. I supported member coordination, delegation, decision-making and international collaboration, which gave me early experience in ownership, communication and working across cultures.
IAMO · Contract Based · Germany
Research Data Assistant
I started working at IAMO alongside my university studies, supporting research-oriented datasets, data entry, data cleaning, online statistical platforms, household survey data, and tools such as Excel and Python. After finishing my studies, I continued this work on a flexible part-time/project basis, including during my internships at FedEx Express and DB Schenker. This experience strengthened my attention to detail and taught me how important reliable data preparation is before any analysis can be trusted.
Reason work paused: The project phase later ended, and in 2025 a new research-data project appeared where my expertise with data preparation, Python workflows and research datasets was needed again. This led to my current part-time contract role at IAMO, described later in the timeline.
FedEx · Internship · Netherlands
Business Intelligence Analyst Intern
Worked with the Customer Engineering / Service Standard & Solutions Europe team on Python data preparation, Power BI dashboard support, customer/shipment trend analysis and reporting automation.
Main contributions:
- Consolidated FedEx/TNT customer and shipment datasets using Python for BI and customer trend analysis.
- Prepared dashboard-ready data by cleaning duplicates, missing values, inconsistent fields and location attributes.
- Added latitude/longitude data by matching origin location codes with postal-code, GIS and internal location sources.
- Supported Power BI dashboards with customer filters, origin/destination views, service-type breakdowns and map-based visuals.
- Automated operational Excel outputs for manifest and data-exchange checks.
- Generated Python-based charts, tables and PowerPoint-ready materials for recurring shipping-profile reporting.
DB Schenker · Internship · Germany
Data Analyst Intern
Worked on ERP and product-data enrichment for DB Schenker’s On-Demand Production Venture. The internship focused on turning inconsistent ERP/Excel exports into cleaner, richer and more usable product datasets for reporting, pricing and supplier/API-supported analysis.
Main contributions:
- Mapped inconsistent ERP and Excel columns into a reusable product-data schema covering part IDs, names, materials, dimensions, weights, costs, demand and lead-time fields.
- Performed Python-based EDA and data-quality checks for missing values, duplicates, semi-duplicates, negative/zero values and inconsistent product records.
- Cleaned and standardized German product-description text to support matching and enrichment.
- Enriched product records using material databases, master datasets, fuzzy matching, material-code lookups and density information.
- Extracted and validated dimension fields from part names and dimension columns, including volume and weight/density consistency checks.
- Prepared API-ready product records for supplier pricing and lead-time calculations using material codes, volume, bounding boxes and quantity fields.
- Compared supplier/API pricing and lead-time outputs against ERP cost and lead-time values.
- Exported structured Excel outputs and review files for stakeholder validation and process-improvement decisions.
DB Schenker · Full-Time · Germany
Data Solutions Developer / Data Analyst
Worked in the On-Demand Production Venture on Python automation, ERP data preparation, supplier API workflows, data quality standards, Power BI reporting and internal workflow tools.
Main contributions:
- Automated repetitive ERP and product-data preparation tasks using Python, reducing manual cleaning, checking and formatting work.
- Prepared, validated and enriched operational data for reporting, pricing, quotation and request-processing use cases.
- Built a modular Flask-based internal workflow tool for customer part requests, covering Outlook email intake, attachment handling, request/deal tracking, supplier selection, quotation preparation, order preparation, Excel outputs and PDF generation.
- Structured request, offer and order data using request IDs, deal IDs, unique IDs, supplier-status columns, customer fields, material/process fields and pricing/order fields.
- Automated supplier and customer communication steps through Outlook draft emails, supplier Excel request files and offer/order PDF outputs.
- Built Power BI KPI dashboards from ERP and company database extracts, reducing management reporting turnaround from days to hours.
- Prototyped human-reviewed AI-assisted extraction and classification workflows for request details, technical files and product-attribute enrichment.
- Documented workflows and supported business users during adoption.
Employer-validated impact:
- Approximately 80% less manual data preparation.
- Reporting turnaround shortened from days to hours.
- 50–60% reduction in customer response time for parts requests.
Reason role ended: The role ended due to post-acquisition restructuring following DSV’s acquisition of DB Schenker, after the venture division was discontinued.
IAMO · Part-Time Contract · Germany
ETL & Data Automation Analyst - Part-time Contract
This is the follow-up IAMO project mentioned earlier in the timeline. I started this part-time contract role alongside my DB Schenker position and continue it today. My work focuses on a Python-based research-data automation pipeline for agricultural economics research. The project combines FAOSTAT production, price and exchange-rate data, UN Comtrade trade data, WTO/RTA metadata and HS commodity mappings, then turns them into structured Excel outputs for research review.
Main contributions:
- Built modular Python workflows for FAOSTAT bulk data, UN Comtrade API extraction, RTA data processing, exchange-rate preparation, reference-price calculation and final workbook generation.
- Standardized country, commodity and year-level datasets across external sources with different naming conventions, codes and formats.
- Mapped FAOSTAT items to HS commodity groups and prepared production, price, exchange-rate and trade datasets for downstream calculations.
- Implemented validation checks for missing values, duplicate records, inconsistent country/product mappings, time-series gaps, source-data issues and unmapped HS codes.
- Handled currency normalization, including exchange-rate preparation, currency redenomination handling and euro conversion logic where needed.
- Added RTA-related logic to identify trade flows affected by regional trade agreements, using deterministic checks and LLM-assisted extraction for partner/date enrichment.
- Generated per-country and consolidated Excel workbooks for reference prices, non-RTA trade indicators and Market Price Support calculations.
Project outputs:
- Research-ready country, commodity and year-level datasets prepared from multiple international data sources.
- RTA-annotated trade data for non-RTA reference-price analysis.
- Structured Excel workbooks for reference-price and Market Price Support calculations.
Project note: The research project is still in progress, so this section focuses on the technical data work rather than unpublished findings.
Available for full-time roles
Next challenge
I am open to Data Analyst, BI Analyst and analytics automation roles where I can improve reporting, automate manual workflows and help business users work with cleaner, more reliable data.
Technical skills
Tools and methods I use most often.
Python & ETL
- Data cleaning, transformation and preparation with pandas and NumPy
- Automation of repetitive data-processing workflows
- API integration and file-based data enrichment
- Validation checks, rule-based logic and error reduction
- Exploratory analysis and visual summaries for business questions
Excel & Business Outputs
- Advanced formulas, lookups and structured data preparation
- Pivot tables, reporting templates and business-user-friendly outputs
- Data validation, conditional checks and consistency reviews
- Cleaning and reshaping operational and research datasets
- Supporting teams that work with spreadsheet-based processes
SQL & Structured Data
- Filtering, joining, grouping and aggregating structured datasets
- Preparing clean extracts for Power BI, Excel and Python workflows
- Checking consistency across tables and data sources
- Creating KPI-ready datasets for dashboards and reporting
- Writing clear queries for analysis and validation tasks
BI & Reporting
- Interactive dashboards for operational and management reporting
- KPI views, business-focused summaries and performance monitoring
- Clean report layouts with clear visual hierarchy
- DAX measures and calculated fields for KPI reporting
- Dashboard structures designed for business users
AI
- Human-reviewed LLM-assisted extraction and classification for request details, technical files, product attributes and RTA metadata, combined with deterministic validation checks.
- Combining AI with Python, Excel, SQL and Power BI workflows to improve speed, consistency and decision-readiness
- AI-Assisted Automation
- Multi-Agent Orchestration
Working style
How I work when the process is complex.
Across my recommendation letters, the same pattern appears: I understand unfamiliar processes quickly, work independently and carry tasks through carefully.
My goal is not only to produce a correct result, but to leave behind a transparent, documented solution that colleagues can trust and use.
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Understand the process
I clarify the business need, map the current workflow and identify where errors, delays or manual effort enter.
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Build with structure
I break the problem into clear steps, validate the assumptions and data, and keep the logic transparent while working independently.
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Make the result usable
I document key decisions, communicate progress clearly and hand over an output that non-technical colleagues can understand and repeat.
Languages
- Russian:
- Native
- English:
- C1 - professional working proficiency
- German:
- B2 - currently improving
How I think about data
A dashboard is only useful when the underlying data, assumptions and workflow are clear. I try to make data work understandable, repeatable and practical for the people who depend on it.