MapYourProperty Inc
Toronto, ON
Senior Data Engineer
Employee Status: Full-time, Permanent, Remote

Why MapYourProperty?
MapYourProperty has a big goal – we’re out to change the way professionals in the real estate business find and assess development opportunities. Our mission is to turn a dinosaur industry on its head with a cutting-edge cloud platform and we’re off to a great start. MapYourProperty is developing predictive pricing for both residential and commercial real estate. Our focus will be to develop a scalable ELT data pipeline and online platform across 16 cities in Canada. We are looking to hire a Backend Engineer who has a strong expertise in AWS and DevOps with interest in real estate analytics and prescriptive automation. MYP is backed by Techstars, largest seed VC in the United States and Colliers International, the largest commercial brokerage in the world.

What We Are Looking For
5+ years experience
Comprehensive knowledge of modern data science and product analytics tools and techniques such as Python, advanced statistics, and data mining techniques
Hands-on experience with Amazon’s AWS, Dagster, PostGIS ecosystems
Hands-on experience with SQL, Python, Rel

Key Knowledge & Skills
Design and develop secure, scalable, high-performance and reliable data pipeline ELT system (with Dagster & PostGIS)
Use Data & Analytics to help provide real estate and housing analytics and to provide insights and predict real estate housing prices and ranked-scoring of properties
Design, extend and review software architecture – be hands-on and involved with every stage of the product development life cycle
Build high quality, testable, and scalable backend systems including REST APIs, batch jobs, and system integrations
Work with the data engineer and geospatial team to build a large data pipeline and warehouse to provide insights to real estate customers across multiple cities in North America
Develop state-of-the-art code – influence/establish the software development culture of the team. Establish standards and best practices for data science & engineering