Geospatial Data Engineer - Climate Change

Who we are: First Street is the industry standard for physical climate risk data. We use transparent and peer-reviewed methodologies to calculate the past, present, and future climate risk for every property in the world. We started eight years ago by working with the world’s leading climate scientists to create groundbreaking, climate-adjusted, property specific models and haven’t stopped. 

Our mission: We exist to connect climate change to financial risk

Our data: We create physics-based, deterministic models of flooding, wildfire and hurricanes, and advanced statistical models of extreme heat, air quality, drought, hail, severe convective storms, winter storms, and more. All of this data is used to create property-level financial risk metrics and macroeconomic variables to quantify the impacts of climate change. 

Our customers: We empower governments at the highest levels to make smart regulations, businesses to avoid bad investments, and everyday Americans to understand their personal risk from climate change. We are relied on every day by:

• Agencies ranging from the U.S. Department of Treasury to Fannie Mae

• The world's biggest banks such as Bank of America and Wells Fargo 

• Institutional investors like Nuveen and Blackstone 

• Millions of users on Redfin, Realtor.com, Homes.com, and more 

We believe:  Our work needs to match the pace and scope of the climate problem. This is why we have invested tens of millions of dollars into our science, data, people, and products and have raised tens of millions more to move even faster. 

Come join us and use your talents to create solutions to address humanity's biggest problem.

Team & Role Overview:

We are looking for a Data Engineer with experience working with Geospatial Data to join our team. The successful candidate will be someone who deeply cares about the environment, loves information technology, and appreciates the importance of data for the success of the First Street mission. They will assist in the ingestion of climate risk and ancillary data from First Street modelers and data partners, develop data pipelines, query geospatial databases, calculate applicable statistics, implement Quality Assurance and Quality Control processes, utilize geographical imagery, and ensure that the Data Team’s databases and pipelines are coordinated and synchronized with the Software Engineering Team’s databases, APIs, and web services. This individual will be responsible for developing data operations across the Data Team, and through their expertise and leadership generally enabling all members of the Data Team to be successful in their roles.

What you’ll do:

• Provide technical support in the processing, analysis, and interpretation of geospatial observations and modeling data.

• Process large volumes of flood and wildfire risk prediction data to improve risk assessment quality and accuracy.

• Plan, execute and direct UNIX-based workflows on local and cloud-based clusters, using GDAL, PostgreSQL, Python, and QGIS.

• Analyze raster and vector data at scale to improve model accuracy, identify quality control issues, and develop suggested remedies for identified issues.

• Perform statistical analysis to validate hazard model predictions and assess model uncertainties.

• Design and implement quality assurance checks on the climate risk data and derived statistics

• Assist in resolution of customer support issues through quality control checks and explanation of the models and risk statistics

What you’ll need:

• Bachelor's Degree in Data or Climate Science, or a related Field

• 2+ year of professional experience

• Data operations: Experience with the design, maintenance, and use of geospatial databases, such as PostgreSQL

• GIS knowledge: Experience with working with spatial data and GIS software such as QGIS

• Programming: Proficiency with SQL queries to efficiently and reproducibly analyze complex datasets preferred. Additional languages like Python or R also required

• Strong understanding of probability and statistics as applied to spatial data

• Expertise using scripted languages to build data pipelines on both local and cloud-based systems

• Experience with big data analysis, parallel processing, and batch/spot workflows on cloud platforms including AWS, GCP, and/or Azure

• Proficiency wi