Our client is transforming complex climate science into transparent and actionable insights for their customers across financial services. Their SaaS platform provides asset-level historic and predictive analytics on climate physical risks.
The team is comprised of Earth system scientists, geospatial data engineer and leaders from financial services. They recently closed a seed round of funding from multiple well-known investors and are looking to double the team in the next 6 months.
This is a great time to join and industry leading, mission-driven team working on frontier tech to use environmental data to solve generational problems adapting to climate change.
Engineer efficient, adaptable and scalable data pipelines to process structured and unstructured geospatial data
Maintain and rethink existing datasets and pipelines to service a evolving variety of use cases
Develop subject-matter expertise in the cloud platform domain
Act as a thought partner and lead on platform engineering, understand the challenges, and make opinionated recommendations to efficiently scale our infrastructure and tools
Enable smart analytics by building robust, reliable, and useful data sets that can power various analytic techniques like spatial regression, super resolution, clustering etc.
3-5 years of experience working on data intensive cloud-native software products
Demonstrable expertise with complex SQL
Understand how to design and develop REST APIs that serve customers and also web applications
Strong familiarity with cloud native platform components including CloudSQL, Cloud data proc, kubeflow or similar
Understand the Data Lifecycle and concepts such as lineage, governance, privacy, retention, anonymity, etc.
Experience building and deploying production software on Google Cloud or AWS.
Expert in engineering data pipelines using big data technologies (Spark, Beam, etc.) on large scale data sets demonstrated through deployed scalable products
Experience working on data analytics on geospatial data in different formats: netCDF and geojson