The Machine Learning Engineer owns the end-to-end data lifecycle for geospatial products, designing pipelines and managing infrastructure. Responsibilities include building automated ETL workflows with Airflow, computing spatial metrics using PostGIS, and developing interactive map layers with Mapbox. Candidates require at least three years of experience in a data science or data engineering role, along with proficiency in Python, Docker, Kubernetes, and advanced SQL.
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.
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