I’m Mykyta — a data analyst who grew into analytics engineering by quietly owning every part of the stack at one company until the title caught up with the work.
The work
Today I work on data infrastructure: ingestion, warehouse modeling, dashboards, and the operational alerting that surrounds them. The interesting part isn’t the tools; it’s deciding which numbers a team should look at, in what order, and what to do when one of them moves.
How I got here
I started with minimal SQL. There was no formal data role to grow into — the company was small enough that the analytics function was a Slack thread and a shared spreadsheet. Over two years I rebuilt that into a real warehouse, a dbt project, and a Looker instance with stable definitions.
The transition from “the SQL person” to “the analytics engineer” happened gradually and then, for purposes of the job title, all at once.
The stack I reach for
- Warehouse: BigQuery (or Snowflake, fine), modeled in dbt as a Star Schema.
- Ingestion: Airbyte or Fivetran for SaaS sources, custom Python for the weird stuff.
- Serving: Looker for governed metrics, Streamlit / Metabase for one-off exploration.
- Alerting: a small Python service with Slack routing. Per-metric thresholds; quiet hours per audience.
- Operating: Git for everything. Code review on every change to a metric. A spreadsheet is acceptable input but never an artifact.
What I’m looking for
Roles where the analytics function is real but small enough to need ownership, not just dashboarding. Companies between 30 and 200 people, post-product-market-fit, with a leadership team that wants to make decisions on real numbers and is willing to fund the modeling work to get there.
Also: open to consulting engagements (see what I do).
Outside that
I run a side project on Formula 1 telemetry, which is what side projects are for — being useless in a happy way.
Education: VŠFS, Prague.