Data Engineer

We are Building Atlas. Our vision is a fully sustainable commercial real estate sector. Our mission is to make this a reality through rapidly scaling building retrofits: the most sustainable building is the one that already exists. With a zero-touch approach, requiring nothing but the addresses of buildings, we help owners of commercial real estate portfolios: (1) find the best path to retrofitting their buildings; (2) avoid stranding risk and increase the value of their assets; (3) and get access to preferential financing and delivery partners to get the work done.

Our goal is to get as many buildings as possible retrofitted, so we recommend retrofit measures that bring the highest return on investment. Choosing the right building to focus on requires understanding its physics (building envelope and the energy systems), its current lease terms and use, its current value and future “green premium,” and the value of energy flexibility in a future grid. We gather all this data and build machine learning models to find the optimum retrofits across a portfolio of buildings.

To make this possible, we’re building a diverse team of people passionate about solving climate change in the built environment. We’re a hybrid team, with office space in London and Edinburgh where we meet regularly.

As a Data Engineer at Building Atlas, your primary role will be managing the data ingestion and storage platform that allows us to train our models and advise our customers how best to move their properties to being fully sustainable. Your work will involve the ingestion of countrywide data on buildings and the physical environment, then cleaning and transforming this to make it trustworthy and easy to use. As we’re a small team, you will also be a flexible individual and will contribute to other technology areas, handling both the data engineering and broader software development tasks effectively.

As a Data Engineer at Building Atlas, your primary role will be managing the data ingestion and storage platform that allows us to train our models and advise our customers how best to move their properties to being fully sustainable. Your work will involve the ingestion of countrywide data on buildings and the physical environment, then cleaning and transforming this to make it trustworthy and easy to use. As we’re a small team, you will also be a flexible individual and will contribute to other technology areas, handling both the data engineering and broader software development tasks effectively.

Key responsibilities:

• Creating robust, performant data pipelines in Python to efficiently ingest, transform, and store data
• Cleaning and pre-processing of messy real-world data
• Designing and implementing appropriate data storage solutions, ideally cloud-based solutions (we use GCP)
• Extracting data from external APIs and integrating that data into our platform
• Utilising version control systems (i.e. Git) and CI/CD pipelines to aid collaboration and fast deployment
• Participating in code reviews, sharing knowledge and feedback with peers to improve code quality and promote best practices.
• Contributing to general software engineering or ML tasks, when required

• 2+ years in a Data Engineering position (or Software Engineering/Data Science position, with a significant Data Engineering component)
• Demonstrated experience creating and deploying robust data pipelines
• Experience working with externally generated datasets
• Familiarity with data storage technologies (e.g., relational DB, object stores, warehouses, NoSQL)
• Experience with cloud platforms and cloud-native data services
• Proficiency in Python and SQL
• Strong analytical and problem-solving skills with the ability to work on complex data challenges

To be able to contribute flexibly to our organisation, having experience in one of the following would be highly desirable:

• Writing production-quality Python code in a software application
• Working with ML models and deploying them

Additionally, it would be advantageous (but not essential) if you have experience with geographic or urban datasets.