I’m a data analyst and analytics engineer based in Prague. I own the full analytics stack at DIM 9000 — pipelines, warehouse, dashboards, alerting — and I consult for early-stage startups that need the same but can’t justify a full-time hire yet.
The work
At DIM 9000, a property management SaaS, I am the analytics function. Everything from ETL to the CEO dashboard to the anomaly alerts that fire at 2am when something breaks — that’s mine.
The stack is GCP-native: PostgreSQL source, Python Cloud Functions for ingestion, BigQuery for the warehouse, BI tools on top. The alerting layer I built from scratch — BigQuery scheduled queries, Cloud Functions, Slack webhooks — designed to be completely quiet when things are fine.
I joined as a part-time intern in July 2025, helping with dashboards and ad-hoc SQL. By October I had taken on the full analytics direction solo — not because I asked for the title, but because the work needed doing and I was the one doing it.
How I think about problems
I’m the kind of person who won’t let a problem go until it’s actually solved — not patched, solved. That stubbornness comes in handy in data work, where the real issue is almost never the first thing you find.
At the same time I try to stay honest about what’s useful. A beautiful pipeline that nobody acts on is just an expensive hobby. I want the work to matter to the business, not just to me.
The builder part
Somewhere between engineering and creativity is where I’m most comfortable. I built this website because I wanted to understand how it should work, not because I needed one. At work I’ve coded tools for myself — small automations, monitoring scripts, shortcuts that don’t exist anywhere else — because building the right tool is faster than tolerating the wrong workflow.
That instinct started with Formula 1. F1 produces enormous amounts of telemetry data — lap times, sector splits, tyre compounds, weather — and someone built FastF1, a Python library that surfaces all of it. I started playing with it and realized that data stops being abstract when it’s attached to something you actually care about. The F1 dashboard project came out of that.
The stack I reach for
- Warehouse: BigQuery, Star Schema — fact + dimension tables, one source of truth
- Ingestion: Python Cloud Functions syncing PostgreSQL → BigQuery every 15 minutes
- Orchestration: Cloud Scheduler + Cloud Functions v2 on GCP
- Serving: BI dashboards for operations and finance; Amplitude for product analytics
- Alerting: Custom anomaly detection pipeline posting to Slack
- Tooling: Git for everything, SQL for thinking, Python for automation
Currently exploring dbt for the transformation layer.
Outside the work
Economics and Management student at Vysoká Škola Finanční a Správní in Prague (class of 2028), with a focus on business analytics.
Languages: Ukrainian and Russian (native), English (C1), Czech (B1 and improving).
If the race is on, I’m watching it.