Job Description:
We are looking for a passionate data engineer to help our product and modelling teams develop asset level and exposure data assets, as well as improve name matching abilities.
Our modular climate risk offering is built to meet the complex needs of insurers, banks, corporates, and real estate investors. Through the SaaS platform, Location Risk Intelligence, we deliver an advanced geospatial solution that empowers clients with location-specific climate risk data worldwide, enabling them to make informed decisions on climate risk reporting, underwriting, and investments.
Key Responsibilities:
* Research, structure and analyze the creation of an asset level data asset from publicly available and purchasable datasets – or through the usage of GenAI capabilities – to improve a new climate risk solution analyzing the physical climate risks of a universe of listed and unlisted companies
* Identify commercial or industrial occupancy of different POIs via publicly available or purchasable datasets to provide building-specific information at a global scale
* Improve the accuracy of existing name matching algorithms and overcome accuracy and performance limitations
* Help to enhance, clean and structure large datasets for different use cases (e.g. Real Estate Investment Trusts, Company Datasets, Biodiversity Areas)
* Support with ad-hoc client requests that require data processing & cleaning, and help create data analysis examples for content marketing purposes
* Work closely with the product and modelling teams overseeing the solution development to ensure high-quality delivery
* Ensure documentation of technical project aspects, including methodology, model and software code
Requirements:
* 5+ years of hands-on experience in data engineering
* Prior experience in GIS engineering or climate risk analysis
* Experience with asset-level databases
* Strong knowledge of data analysis, GenAI, as well as asset level and exposure datasets
* Strong knowledge of name matching algorithms
* Extensive experience working with large datasets
* Proficiency in relevant programming languages (e.g. Python, SQL, R, MATLAB)
* Proficiency in relevant data analysis tools (e.g. Databricks, Spark)
* Nice to have: any experience with geospatial stack (e.g. Postgis, QGIS, ArcGIS)
* Strong analytical mind, problem solving skills and ability to think creatively
* Team player with strong motivation for interdisciplinary and intercultural collaboration