PriceHubble is a PropTech company, set to radically improve the understanding and transparency of real estate markets based on data-driven insights. We aggregate and analyse a wide variety of data, run big data analytics and use state-of-the-art machine learning to generate stable and reliable valuations and predictive analytics for the real estate market. We are headquartered in Zürich, with offices in Paris, Berlin and Tokyo. We work on international markets. We are backed by world-class investors. We have a startup environment, low bureaucracy and international team and business.







Data engineers are the central productive force of PriceHubble. As a Senior data engineer, your mission will be to guide the data-engineering work in PriceHubble. You will be given the responsibility for substantial parts of our data engineering systems. Your daily challenges will be to mine a wide range and variety of new datasets of all sort. Doing so will expose you to a wide variety of tasks ranging from building the infrastructure (Spark on Kubernetes), to building machine-learning models extracting features from raw data, to generating pipeline to process and expose new data sources,





Your Mindset

You are convinced that success in data science is achieved via data-monopolies. You are highly motivated to join an organization who is committed to building the best in class data-engineering software for acquiring, processing, and enriching real-estate data.

The following challenges speak to you:

gather vast amounts of data about real estate

consolidate, improve, and link this data to generate data sets no one else has on the market

do that all over the world

You are keen to join a startup right in its growth phase, and are not afraid to refactor code to get it to the new engineering standards that will support the growth of the organisation.



At work, your team is your main asset: you are keen to mentor fellow team members. In the startup, you are committed to create the company you want to work in; in terms of competence, standards, and mindset.





Responsibilities

Extract, cleanup, structure and transform complex raw and processed datasets to extract insights from it

Retrieve a wide variety of datasets and integrate them into the data pipeline

Create and maintain an efficient data infrastructure

Build data enrichment pipelines, using machine-learning when appropriate

Continuously provide new ideas to improve our engines and products

Requirements

MSc in Computer Science or equivalent

At least 3 years of experience in a similar position

Proficiency in Python and at least one scripting language

In-depth understanding of basic data structures and algorithms

Familiarity with software engineering best practices (clean code, code review, test-driven development, ...) and version control systems

Experience with the ETL and data processing tools we’re using is a strong advantage: PySpark, PostgreSQL, Luigi

Working experience with cloud providers (Google cloud, AWS or Azure)

Advanced knowledge of relational databases

Experience with Docker and Kubernetes orchestration is a strong advantage

Understanding of core machine learning concepts is an advantage

Worked previously in ‘agile’ team(s) and are looking forward to doing it again,

Comfortable working in English; you have a great read, good spoken command of it.

Benefits

🕓Flexible work hours

👖Casual dress code

🍏Free snacks, fruits, coffee, beers, sodas

🍺Thursday drinks

✈️Relocation package



📘L&D program

🏢Well-located offices



💰Competitive salary