About & Experience

Background

How I got into data work

I’m from Estonia and now live in Halle, Germany. I studied Business Economics at Martin Luther University Halle-Wittenberg and have worked with data across economic research, logistics and on-demand production.

My first data role was at IAMO, preparing survey and statistical datasets for researchers. At FedEx and DB Schenker, I moved into reporting, automation and internal tools.

I enjoy work that starts with a practical problem: a report that takes too long, data that needs checking or a task someone repeats by hand. I like understanding the process and finding a better way to handle it.

Experience

2025 – Present

IAMO · Part-time contract · Germany

ETL & Data Automation Analyst

I build Python workflows that collect, check and combine international production, price and trade data for economic research. The pipeline brings together sources such as FAOSTAT and UN Comtrade and produces Excel datasets for analysis.

I align countries, product categories, years and currencies across sources, with checks for missing values and inconsistent records. I also use AI-assisted extraction, automated checks and manual review to organise information about trade agreements.

In development: I’m building an AI research assistant with Python and LangGraph to support literature review, data analysis and drafting, with source tracking and human approval.

2023 – 2025

DB Schenker · Germany

Data Analyst — Automation, BI & Internal Tools

I worked in DB Schenker’s On-Demand Production Venture, first as an intern in 2023 and then in a full-time Data Analyst role in 2024–2025. During the internship, I used Python to match product records with reference data and fill in missing material and dimension information for supplier pricing.

In the full-time role, I automated ERP and product-data preparation, reducing manual preparation time by approximately 80%. I used SQL to prepare operational data and built Power BI models and dashboards that shortened turnaround for key reports from days to hours.

I also developed a Python/Flask tool for customer parts requests, quotations and orders, helping reduce customer response times by an estimated 50–60%. I connected supplier APIs to calculate prices from customer and product data.

My work also included integrating AI APIs into prototypes for extracting product information from technical drawings and CAD files, with human review.

2022

FedEx Express · Internship · Netherlands

Business Intelligence Analyst Intern

I used SQL and Python to combine customer and shipment data from FedEx and TNT for Power BI reporting. I supported reports on customer trends, shipment volumes, service types and shipment weights.

I added location data for map-based reporting and automated Excel checks of shipment records for operational reviews.

2020 – 2023

IAMO · Part-time contract · Germany

Research Assistant

I cleaned and checked survey and statistical datasets for economic research using Python and Excel. I also created charts, tables and other visual materials for research outputs.

I started this role alongside my university studies and continued on a part-time, project basis after graduating.

2017 – 2021

Martin Luther University Halle-Wittenberg · Germany

B.Sc. Business Economics

2018

AIESEC Halle · Volunteering · Germany

Team Leader, Global Outgoing Exchange Team

I coordinated team members, delegated tasks and supported international exchange activities alongside my studies.

Present

Available for full-time roles

What I’m looking for

I’m looking for a full-time role in data analysis, business intelligence or data automation, where I can help teams answer business questions, improve reporting and build useful tools.

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.

Understand the process

I clarify the business need, map the current workflow and identify where errors, delays or manual effort enter.
Build with structure

I break the problem into clear steps, validate the assumptions and data, and keep the logic transparent while working independently.
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

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.