Senior Data Engineer

Company & Mission Overview:

Our mission: We exist to connect climate and financial risk.

Who we are: First Street is the standard for Climate Risk Financial Modeling. For over a decade, we’ve been translating climate risk into decision-useful financial outcomes for investors, businesses, communities, and property owners worldwide. With backing from world-class firms, including Innovation Endeavors, Galvanize, General Catalyst, and others, our team has raised millions to change how the global economy thinks about climate change.

Read more about our culture here and see what Climate Risk Financial Modeling is all about here.

Our data: We’ve assembled leading climate scientists and economists to develop transparent, peer-reviewed methodologies to calculate the past, present, and future climate risk for properties and asset classes spanning real estate, infrastructure, and companies. Using physics-based deterministic models, we predict the likelihood of floods, wildfires, hurricanes, and other hazards at any location on Earth, along with associated damage and downtime.

Our customers: We aim to incorporate climate risk data into every financial decision made today. We are relied on every day by:

• Institutional investors like Norges Bank Investment Management and Blackstone.
• Banking enterprises, including Bank of America and Fifth Third.
• Government bodies ranging from Fannie Mae to the US State of Connecticut.
• Millions of everyday users on Zillow, Redfin, Realtor.com, Homes.com, and more.

Come join us and use your talents to change the world.

Team & Role Overview

We are looking for a Senior Data Engineer 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 databases and pipelines coordinate and synchronize with APIs, and web services. This individual’s expertise and leadership will enable all members of the Data Operations 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.
• Develop and implement processes for processing large volumes of hazard prediction data to improve risk assessment quality and accuracy.
• Plan, execute and workflows on local and cloud-based environments, using technologies such as GDAL, PostgreSQL, Python, Spark
• 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
• 5+ years of professional experience
• Data operations: Experience with the design and use of databases, such as PostgreSQL
• Programming: Proficiency with SQL queries to efficiently and reproducibly analyze complex datasets preferred. Additional languages like Python also required.
• GIS knowledge: Experience with writing software to efficiently process and analyze with geospatial data using open source tools
• 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
• Proficiency with source control platforms such as Git
• A science-based approach with a high degree of concern for reliability, accuracy and reproducibility
• Experience in GIS and/or geospatial statistical analysis
• Nice to have: experience in DBT and Spark

What will make you stand out:

• Previous experience in the physical sciences
• Masters Degree Preferred
  
  
  Our anticipated US base salary compensation range for this role is $100,000-175,000 plus stock options and a competitive benefits package, which includes equity, 401(k), paid time off, paid parental leave, and comprehensive health benefits. Actual compensation will vary depending on factors such as work location as well as additional factors such as a candidate’s qualifications, skills, experience, competencies, and relevant education. Your recruiter can share more about the specific salary range for your location during the hiring process.

How we work:

• Impact: We only focus on things that move the needle
• Drive: We are driven by the role we play in connecting climate change to financial risk
• Ownership: This is our company and we act accordingly
• Urgency: We move quickly because the world depends on it
• Resilience: We have a growth mindset in all that we do

What we offer:

• Competitive salary commensurate with experience
• Ownership interest in the company via Employee Stock Option Plan
• Hybrid Schedule with in-office work days on Monday, Wednesday and Thursday
• 15 vacation days along with 8 statutory company holidays, 5 days for winter break office closure, and 10 sick days
• Healthcare monthly premium covered at 100% for employee or a significant contribution for family plans
• Vision and dental benefits with partial employee contribution
• 12 weeks of paid parental leave
• Access to One Medical, Teledoc, HealthAdvocate, Kindbody, and Talkspace
• Company 401k program
• Commuter benefits
• Life Insurance
• Tech startup environment
• Weekly team meals and an office stocked with coffee and snacks
• Working on the world’s biggest issue with other passionate professionals

We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.


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