About Me
I think most reporting fails not because of bad tools, but because nobody treated the data layer like it mattered. I'm a BI developer and analytics engineer who builds production-grade Power BI environments — the kind where the numbers are trusted, the logic is transparent, and leadership actually uses them to make decisions. I come from fintech and quantitative economics, and I bring that rigor to every model, metric, and interface I ship.
Philosophy: Systems, Not Static Structures
Companies behave less like fixed structures and more like living systems. They react, fluctuate, and evolve. Revenue moves in cycles, customers adapt, and markets shift.
Beneath that motion, however, there is structure: dynamics that can be modeled, abstracted, and understood.
A dashboard metric, then, isn’t “just a number.” It’s the visible surface of a deeper system. It's an expression of the underlying data architecture, modeling logic, and design choices, condensed into a single signal.
I design and build abstractions that are technically sound and aligned with how people actually make decisions.
In practice, this means designing Power BI dashboards that surface the right signal at the right moment — not just more data.
My Focus
- 01
Systems Thinking
Understanding the whole, not just the parts.
- 02
Analytics Engineering
Building robust, scalable data pipelines.
- 03
Modeling & Forecasting
Predicting outcomes with statistical rigor.
- 04
Decision Interface Design
Visualizing complexity for clarity.
Selected Experience & Systems Built
With 3+ years of experience building and operating production analytics systems in high-stakes fintech environments, my core focus is owning the Power BI decision layer — the point where data becomes trusted, repeatable business action.
I’ve owned and operated production Power BI environments spanning 10+ dashboards used daily by executive leadership, sales, and operations. This included end-to-end responsibility for semantic modeling, KPI logic, access control, performance optimization, and long-term maintainability — treating BI as a product, not a reporting artifact.
Underneath the dashboard layer, I’ve worked extensively on analytics engineering and data modeling: designing metric layers, contributing to dbt-based transformations, aligning source-of-truth definitions, and ensuring downstream reports remain stable as data and business logic evolve. This foundation enabled more advanced use cases such as forecasting systems, scenario planning, and decision support models built on top of the same trusted data structures.
A recurring theme in my work is translating between different stakeholder perspectives and levels of abstraction — from operational teams needing fast, concrete signals, to senior leadership requiring high-level clarity, comparability, and confidence in the numbers. This has shaped both how I design metrics and how I design interfaces.
Beyond analytics logic, I’ve led cross-functional BI redesign initiatives with Design and Marketing: migrating legacy dashboards to a unified visual system, introducing style guides, templates, and shared conventions (Figma / Zeroheight), and raising data-visualization quality across teams to ensure dashboards are not only correct, but usable.
This work is grounded in formal training in economics and quantitative methods — a B.Sc. in Business Administration with a forecasting focus, followed by graduate studies in Quantitative Economics covering time series analysis, econometrics, and statistical modeling.
I’ve been using GenAI and LLMs in production analytics workflows since 2022, and actively explore emerging patterns such as agentic systems, context management, and tool orchestration. This includes connecting data, code, and analytical tools via MCP-style interfaces and agentic IDEs, with the goal of augmenting analytics teams and enabling stakeholders along the decision chain to harness modern AI toolkits effectively.
Power BI Production & Ownership
End-to-end ownership of a mission-critical BI environment.
- Owned and operated 10+ production Power BI dashboards
- 80+ monthly active users across executive, sales, and ops teams
- Board-level, sales, and operational reporting in daily use
- Centralized KPI definitions and semantic models
- Performance tuning, model refactoring, and RLS over time
- Stakeholder alignment, feedback cycles, and long-term ownership
KPI & Metric Layer
Standardizing metrics to restore trust and speed.
- Replaced fragmented reporting with a single KPI layer
- Reduced reporting effort from multi-day cycles to same-day delivery
- Supported executive and investor reporting on €4bn+ annual transaction volume
- Enabled consistent metrics across finance, sales, and leadership
Forecasting & Scenario System
Decision-grade revenue and pipeline forecasting.
- Built multi-variable stochastic revenue models
- Modeled pipeline velocity and conversion dynamics across large funnels
- Combined hazard-based event modeling with seasonal time-series forecasting
- Used in rolling executive planning and scenario analysis
- Covered pipelines in the hundreds of millions in deal value
Lead Scoring → Activation
Turning behavioral data into marketing efficiency.
- Built predictive lead-scoring models from behavioral data
- Integrated scoring into CRM and ad platforms (Reverse ETL)
- Supported six-figure monthly SEA spend
- Optimized routing of high-intent leads to sales teams
- Foundation for performance-marketing optimization
M&A Due Diligence & Financial Modeling
Building investor-grade financial intelligence for strategic decisions.
- Modeled multiple acquisition targets with deal values in the multi-million euro range
- EBITDA uplift quantification and synergy calculation models
- Strategic scenario analyses across buy/hold/exit cases
- Enterprise value and equity stake modeling
- Break-even and threshold sensitivity analysis