Summary
✨ AI‑Generated
Build and evolve a modern analytics warehouse using cloud data technologies. You will create production-grade ETL/ELT pipelines, integrate data from multiple business sources, establish warehouse best practices and data quality controls, and translate business questions into useful data structures.
Highlights
Hands-on data engineering role focused on building and evolving an analytics warehouse, improving data quality and modeling practices, and collaborating closely with product, marketing, and analytics stakeholders.
Description
What you will do
Build and evolve the company's analytics warehouse on top of BigQuery.
Set up data pipelines from product and marketing sources (AppsFlyer, Adjust, RevenueCat, Amplitude, Mixpanel, Facebook Ads, and similar), shape the data into a form that's convenient for analysts and product managers, and document the data model.
Bring warehouse best practices into the company — layering (raw / staging / marts), naming conventions, versioning, data quality tests.
This role isn't purely technical: it's important to talk to product, marketing, and analytics people, understand their questions, and translate business needs into data structure.
Must-have
2+ years of hands-on data engineering experience, or a strong analytics role with a real engineering component (solid junior / light middle level)Confident Python for writing ETL/ELT scripts, working with APIs, and automating pipelines (not "read a book," but actually shipped production code)Experience with GCP (BigQuery, Cloud Functions, Cloud Storage)SQL at a confident level: window functions, CTEs, query optimizationUnderstanding of analytics warehouse principles: layering (raw / staging / marts or a similar model), normalization vs denormalization, pipeline idempotency, incremental loadsExperience integrating data from external sources via APIs or off-the-shelf connectorsStrong communication — able to talk to non-technical stakeholders (product managers, marketers, analysts), ask clarifying questions, and explain technical constraints in plain language.
This is critical: half the job is aligning on what data is needed and in what formGit branching, code review
Nice-to-have
Experience with the typical subscription/mobile stack sources: AppsFlyer, Adjust, Adapty, Amplitude, MixpanelUnderstanding of mobile attribution specifics: SKAdNetwork, postbacks, deterministic vs probabilistic attributionExperience with BI tools (Looker Studio, Metabase, Tableau, Power BI) — at least at the level of "can wire up a source and build a dashboard"Terraform / IaC for GCP resourcesExperience with CDC / streaming (Pub/Sub, Dataflow) — not critical, but a plus
What You'll Love Working with US
Hybrid: 2 days office (Warsaw) / 3 days remote — best of both worlds.Work hard, play hard — epic team offsites and events throughout the year.Lunch covered on your office days.Young, driven team with real startup energy — no bureaucracy, no micromanagement.Flexibility where it matters — we care about results, not watching the clock.Room to be creative and bring your own ideas to the table
