Data Business Analyst, BI Engineer, Pharma Experience

This candidate has repeatedly served as the link between business users and technical teams, capturing reporting needs through structured requirement gathering and ensuring clarity across all project phases

Brings 6 years of hands-on experience across academic research and the pharmaceutical industry, covering dashboard delivery, SQL-based data extraction, and analytical reporting in complex, regulated environments

Sound technical knowledge of SQL, Power BI and SAP BW allows candidate to engage credibly with development teams, interpret data models, and turn business questions into concrete reporting solutions

Throughout the career candidate has managed reporting initiatives from initial stakeholder conversations through to dashboard delivery and validation, demonstrating a natural ability to bridge operational needs with data outcomes

Designed and developed Power BI dashboards and reports to deliver data insights to various departments, enabling better business decisions, and shows KPIs

Performed data analysis using Python, including data cleaning, transformation, and visualization to uncover business trends

Collaborated with stakeholders to understand the business objectives and translate them into data solutions

Worked with relational databases and data lakes to access and manage large datasets efficiently

Created ETL processes. Extracted, cleaned, and visualized data including analytical reporting

Wrote SQL queries for data retrieval, aggregation, and performance optimization

Created detailed Excel reports with dynamic charts, pivot tables, and data models to support operational and strategic needs

Achievements: Created PowerBi report based on Veeva database with 420 unique users; Dashboard which collapses 8 different excel tables

Created a report to support the summary of plant efficiency, presenting production volumes, machine operating and downtime, workforce levels, and defined KPIs

Reduced the process of product portfolio analysis by 20%

Extracted data from medical databases in SAP BW to generate reports and support health researchers

Data extraction from medical histories, which supported the processing of unstructured medical texts in the field of lung and prostate cancer

A/B Testing

Analyzed real-world data (RWD), particularly in processing health-related text, using advanced text analysis techniques such as regular expressions

Used NLP and Regex techniques which helped reduce anamnesis processing time by 30%

Analysis of Public Transport Data

Analyzed Country´s long-distance bus network with the goal of improving timetable efficiency and reducing operational costs