Power BI Desktop
Maven Analytics
Formal Power BI Desktop training covering the core workflow behind the BI work shown across this portfolio.
I came into analytics through growth and marketing. That made me care less about reporting for its own sake and more about what changed, why it changed, whether the data can be trusted, and what a team should do next.
Copenhagen-based analytics professional with around four years in dedicated BI/data roles and more than ten years working with data-driven commercial decisions.
These are synthetic recreations of analytical problems I have handled professionally. No employer or customer data is used. The point is the reasoning, modelling and communication, not a screenshot gallery.
How do you connect behaviour, campaign activity and CRM pipeline without accidentally counting the same opportunity value several times?
Turning event data into an onboarding funnel, cohort view and a practical answer to where activation is being lost.
A management view that combines actuals, forecast, budget and scenarios so the conversation can move from “what happened?” to “what now?”.
Owned analytics across three global websites with hundreds of sub-domains. Built KPI structures, Power BI/Looker reporting, event and measurement logic, data-quality controls, and decision routines across Marketing, Sales and Product.
Managed global performance activity with monthly spend above six figures, using conversion, audience and traffic-quality data to steer investment. ROAS exceeded 85% by departure.
Built BI reporting for a flagship affiliate business and owned recurring monthly performance reviews, producing and presenting an 11-page management pack covering results, drivers, anomalies and next actions.
Worked across app and web acquisition, experimentation, event taxonomies, Firebase/MMPs, cohorts, retention, LTV, forecasting and commercial performance.
International advertiser, publisher and partner roles combining performance data, integrations, forecasting and commercial negotiations.
I am strongest in SQL/BigQuery, Power BI/DAX, measurement and stakeholder-facing analytics. Python is mostly AI-assisted and human-validated; I prefer being precise about that rather than pretending every tool is equally deep.
Semantic/star-schema modelling, reusable measures, reporting layers, forecasting, scenarios, segmentation and practical experimentation.
Event taxonomies, funnels, cohorts, retention, attribution, S2S postbacks, deep links, CRM-connected reporting and data quality.
AI for SQL/DAX troubleshooting, analysis acceleration, classification, reporting and prototypes. Outputs are human-validated before they become decisions.
Selected credentials that support the technical and commercial foundations of my work. I keep the distinction clear between course completion and active vendor certification.
Maven Analytics
Formal Power BI Desktop training covering the core workflow behind the BI work shown across this portfolio.
Supports my earlier paid-search and performance-marketing background; shown as a historical credential rather than a current certification.
A dashboard can look finished while the underlying question is still fuzzy. My preferred way of working is to agree definitions, understand what changed, test the likely drivers and make the recommendation clear enough that the meeting ends with a decision rather than another request for data.
I also enjoy building the operating structure around analytics: KPI governance, intake and prioritisation, reporting standards, documentation, adoption loops and the small routines that turn one-off analysis into something a team can trust.