Commercial Analytics Engineer · Berlin
I turn messy commercial questions into trustworthy data products, useful analysis and practical action.
Business question → data signal → decision
A practical approach
From question to signal
01 / Positioning
My move into analytics builds on 10+ years in e-commerce and retail. Buying, merchandising, assortment planning, supplier negotiation and trade performance taught me to look for the decision behind the metric.
Now I pair that judgement with SQL, data modelling, ETL, BI and Python: organising and sense-checking data, investigating changes, and communicating clear findings to technical and non-technical stakeholders.
Background
E-commerce + retail
Strength
Context behind the KPI
Working style
Clear, curious, practical
02 / Selected work
Four projects that show how I turn a business problem into a data product, a signal and a useful takeaway.
Public project visuals are static screenshots captured from the live apps; the Essential visual is an illustrative framework with synthetic figures.
03 / Experience
Essential
Built SQL/dbt models, multiple Metabase dashboards and reporting workflows, data-quality checks and KPI definitions across customer, subscription, payment and operational data.
25 locations · 27,000+ customers · 4,400+ subscriptions
BestSecret Group
Used revenue, margin, sell-through and inventory data to guide pricing, assortment, supplier and stock decisions across a broad brand portfolio.
Commercial impact · ROI · stakeholder recommendations
brands4friends
Monitored campaign, product and commercial KPIs across thousands of styles and turned customer-response data into recommendations for commercial and marketing stakeholders.
Campaign performance · promotion · KPI monitoring
Zalando SE
Progressed into buying while using MicroStrategy BI and advanced Excel for transaction, brand, category and SKU-level analysis supporting purchasing and forecasting decisions.
Assortment · forecasting · operational analysis
Earlier foundation: Sales at BESTSELLER (Jack & Jones), June 2014 — January 2016. Essential examples are intentionally aggregated and exclude confidential client information.
04 / How I work
The useful part is the connection between a business decision, a dependable data foundation and a story people can act on.
Clarify the commercial or operational question before reaching for a chart.
Model, validate and define the KPI so the answer has a foundation.
Translate the pattern into a clear visual, finding or recommendation.
05 / Skills
The technical and commercial skills I bring together on the same brief.
Training · 2025–2026
Analytics Engineering, ETL & Data Analysis
Liora — Sorbonne School of Economics
Approx. 400 hours · 3 certifications
MSc · 2011–2013
Innovation, Knowledge & Entrepreneurial Dynamics, Economics
Aalborg University Business School
BSc · 2007–2010
Business Administration · 110/110 with honours
Università degli Studi della Basilicata
06 / Contact
Have a commercial question, a dataset or a decision that needs a clearer signal?