Machine Learning Engineer

RealAdvisor; España; A distancia; EUR 65.000 - 90.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Fully remote role with flexible hours

International, ambitious team culture

Collaborative leadership and go-to- GT

Descripción de la vacante

RealAdvisor is seeking a Machine Learning Engineer to own geospatial data products and end-to-end data pipelines. You will orchestrate workflows on Kubernetes, build high-performance geospatial visualizations with Mapbox, and ensure production-grade Python code powers real estate insights.

This fully remote role offers ownership, autonomy, and collaboration with an international team across Europe, focusing on scalable data platforms and fast-moving market insights.

Formación

• End-to-end data pipelines with ETL and Airflow.
• Geospatial analytics using PostGIS and Python.
• Infrastructure management in Kubernetes environments.
• Production-grade Python development for data products.

Responsabilidades

• 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 using PostGIS and Python.
• Infrastructure Management: Maintain and optimize the Kubernetes environment where pipelines and models run.
• Visualization Engineering: Transform outputs into interactive map layers with Mapbox on the frontend.

Conocimientos

Airflow

Kubernetes

Docker

Python

PostGIS

Herramientas

PostGIS

ClickHouse

Mapbox

Descripción del empleo

• Orchestration & Operations: Airflow, Kubernetes, Docker

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

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