RealAdvisor is one of the leading digital platforms in the European real estate ecosystem. Founded in Switzerland, we now support 5,000+ real estate agencies and attract 10+ million users per year across Europe.
We are scaling fast across multiple markets with a clear mission: help real estate professionals grow through data, transparency and smart technology. At RealAdvisor, we value ownership, autonomy and impact.
Role Overview
We are looking for a Machine Learning Engineer to take end-to-end ownership of our geospatial data products.
This is not a model-tuning role. You will design, build, and operate the full data lifecycle behind our real-estate insights — from orchestrating complex pipelines on Kubernetes to delivering high-performance geospatial visualizations with Mapbox.
This is a builder role.
You should be as comfortable debugging an Airflow DAG as you are computing spatial statistics or optimizing analytical queries.
You’ll have full autonomy over a modern, high-performance stack:
- Orchestration & Operations: Airflow, Kubernetes, Docker
- Data Storage: Postgres (PostGIS) for spatial workloads; ClickHouse for large-scale analytics
- Programming: Production-grade Python
Requirements
Key Responsibilities
- End-to-End Pipelines: Design and deploy automated pipelines (ETL) using Airflow to ingest diverse datasets (Real Estate transactions, listings, geo-data).
- Geospatial Analytics: Compute advanced spatial metrics (e.g., pricing honeycombs, average prices per neighborhood) using PostGIS and Python.
- Infrastructure Management: Maintain and optimize the Kubernetes environment where your pipelines and models run.
- Visualization Engineering: Transform your analytical outputs into interactive map layers (Vector Tiles) that integrate seamlessly with Mapbox on our frontend.
What We’re Looking For
- Truly Full-Stack (Data): You don’t believe data scientists should stop at notebooks. You own infrastructure, deployment, and reliability because that’s how real value is delivered.
- Strong Geospatial Intuition: You understand projections, polygons, spatial joins, and how to visualize density and patterns effectively.
- Engineering Mindset: You write clean, modular, maintainable Python code designed for production — not one-off scripts.
- Experience: 3+ years in a Data Science or Data Engineering role, with hands-on ownership of production systems.
- Infrastructure Proficiency: Solid experience with Docker and Kubernetes, including debugging and troubleshooting live environments.
- Database Expertise: Advanced SQL, especially with PostGIS or similar spatial extensions. Experience with ClickHouse is a strong plus.
- Orchestration Mastery: Deep hands-on experience with Airflow for building and maintaining reliable data pipelines.
- Fully remote role with flexible working hours.
- Close collaboration with leadership and go-to-market teams.
- International, ambitious, and collaborative team culture.
Benefits
- Fully remote role with flexible working hours.
- Close collaboration with leadership and go-to-market teams.
- International, ambitious, and collaborative team culture.