JH

Turn Data Into Decisions That Drive Revenue

I design end-to-end data & AI systems — from pipelines to predictive models to executive dashboards — that help companies understand customers, forecast outcomes, and act with confidence.

Most companies have data.
Few have decision systems.

Backward-Looking

Dashboards that describe the past but don’t guide action.

Disconnected

Data pipelines that exist but don’t connect to business impact.

Experimental

AI experiments that never turn into usable workflows.

I build the system between
data and decisions.

I combine analytics engineering, AI-assisted workflows, modeling, and visual decision interfaces into one coherent system designed around business outcomes.

Decision System Design

Translate business questions into measurable, model-driven analytics systems.

  • KPI architecture
  • Metrics logic
  • Analytics roadmap
  • Stakeholder alignment

AI-Accelerated Infrastructure

Robust data pipelines and agentic workflows that scale.

  • Data pipelines & warehousing
  • DBT analytics engineering
  • Synthetic data prototyping
  • Agentic workflow automation

Value-Driving Analytics

Move beyond dashboards to predictive and prescriptive insights.

  • Customer segmentation
  • Churn prediction
  • Forecasting & modeling
  • Next-best-action logic

Projects

End-to-End SaaS Operating System
Stochastic Financial Twin

End-to-End SaaS Operating System

A Proof of Concept for programmatically instantiating a commercial-grade data stack from a static business definition, compiling Excel models into a living data system.

Excel Python dbt DuckDB SQL Streamlit Prophet

The Concept

This project implements a "Rapid Data Function"—instantiating an entire end-to-end data stack (Stochastic Simulation, Data Warehouse, Algorithmic Forecasting, and BI) purely from a static business definition.

Core Architecture

  • Contract-First Engineering: The entire simulation scales dynamically based on a JSON contract extracted from the Excel Operating Plan.
  • Stochastic Engine: Simulates Customer Acquisition (Poisson), Retention (Hazard/Survival), and Revenue expansion.
  • Medallion DWH: A rigorous dbt project structure (Bronze/Silver/Gold) ensuring clean lineage.
  • Regime-Aware Forecasting: A Prophet model that detects "Growth Regimes" to avoid overfitting on early-stage data.

Development Workflow

We treated the Business Contract as code, compiling strategy into a digital twin:

  1. Context: Domain knowledge structured in Obsidian.
  2. RAG Layer: NotebookLM used to synthesize technical specs.
  3. Agentic Execution: Feeding specs into Cursor/Windsurf to scaffold the stack.
Vantage Alpin: Analytics Re-Engineering
Production Data Stack

Vantage Alpin: Analytics Re-Engineering

A Single Source of Truth financial reporting pipeline replacing legacy systems with a modern Python/dbt/DuckDB stack, simulating complex e-commerce dynamics.

Python dbt DuckDB SQL PowerBI PowerQuery DAX

Executive Summary

This project acts as the central financial reporting engine for Vantage Alpin. It replaces legacy PDF reports with a dynamic Modern Data Stack (MDS) that processes stochastic transaction data into a clean Star Schema.

Data Science & Simulation

  • Controlled Stochasticity: Uses Non-Homogeneous Poisson Processes (NHPP) to model realistic demand patterns (Seasonality, Payday effects, Events).
  • Product Economics: Pricing follows Log-Normal distributions; popularity follows Pareto (Power Law).

Analytics Engineering (dbt)

We implemented a strict Kimball dimensional model:

  • Marketing Allocation: Algorithmic attribution of daily ad spend to individual order lines based on revenue share.
  • Currency Normalization: Robust handling of multi-currency transactions (EUR/CHF) with daily rate lookups.
  • Star Schema: Central fct_transactions table linking to Type 1 SCD product dimensions.

BI Architecture

To support local Power BI development without complex drivers, we architected a custom Parquet/CSV export workflow from DuckDB, enabling performant DirectQuery-like experiences.

I see companies less as structures and more as living systems.

They breathe. They react. Revenue fluctuates, customers behave in cycles, and markets shift. Beneath the surface, there is always structure in the motion — dynamics that can be modeled, abstracted, and understood.

To me, a dashboard metric isn’t “just a number.” It’s the visible tip of a deep system: data architecture, modeling logic, and design choices, all condensed into a single signal.

My goal is to design that abstraction so it is technically sound and aligned with how humans actually decide.

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.

Let’s design your decision system.

If your company has data but decisions still rely on gut feeling, we should talk.

Schedule Consultation

Let's Connect

Ready to transform your data into actionable decisions? I'm currently accepting new projects.
Drop me a line and let's discuss your strategy.

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