OUR MISSION

Local Logic is digitizing the built world to make it universally understandable and actionable for consumers, investors, developers, and governments with the ambition of helping build cities that are more sustainable and equitable for the people who live in them.
To achieve that dream, we’ve built a digital twin of cities, quantifying the built world using data and AI to interpret the $217T real estate market throughout the US and Canada.
We started our journey at McGill University’s urban planning department, where we came to see that cities were being developed in all kinds of unsustainable ways. Why were sprawling suburbs still being built, when doing so would increase pollution and inequality? Why were new business parks being built far from mass transit, when doing so would make traffic congestion skyrocket? Why was social housing being built in places that would exacerbate social problems rather than improve them?
It became clear to us why: cities are incredibly difficult to understand. However, we realized that with recent advances in data science, all the complexity of cities could be made simple enough for anyone to understand. And that understanding would be essential to making the sustainable, equitable, and prosperous cities that we so desperately need.
Today, Local Logic delivers sophisticated location insights through webtools, APIs, one-click reports and a data analytics platform. Our insights are powered by billions of data points we’ve generated that describe all aspects of cities - from the distance to the nearest bus stop, the quietness on the street, to the most recent trends in the housing market and more.

KEY REQUIREMENTS:

• At least 5 years of experience in software / data engineering or closely related fields. Experience as an Architect, Staff, Principal or Lead role is an asset.
• Proficiency with Python
• Experience designing, building, and maintaining data pipelines and maintaining a large-scale data ecosystem.
• Experience with cloud infrastructure and data orchestration tools (we use AWS, Databricks, Kubernetes and Dagster).
• Experience working with large-scale geospatial data is an asset.
• This position is for hybrid work in Montreal, with an expectation of 2-3 days per week in office.

• Promote software engineering best practices to build robust data pipelines.
• Provide technical leadership for a small team of data engineers and data scientists responsible for designing, building, and maintaining our data infrastructure.
• Translating proof-of-concepts into scalable, production-ready code.
• Work closely with data scientists, machine learning specialists and data engineers to understand their needs and ensure that the data infrastructure meets them.
• Collaborate directly with the Technical Product Manager embedded in the Data pod to create & refine work tasks for the team.
• Help define and implement best practices for data management, quality, and governance, to ensure data quality, accuracy, and consistency for the team.
• Provide technical guidance and mentorship to the data team.
• Participate in the technical hiring process of future team members.