Data Engineer

Are you a Data Engineer who enjoys working with complex data, building reliable systems, and solving problems at the intersection of technology and real-world impact? At Understory, we’re reimagining how weather and catastrophe risk are measured, understood, and priced, and we’re looking for a versatile Data Engineer to help us build the data infrastructure that makes it possible.

We work with rich sensor data, geospatial datasets, custom risk models, and a fast-moving engineering stack to power insurance products with precision and speed. If you enjoy working with large, messy datasets, building systems that other teams depend on, and moving comfortably between infrastructure, data pipelines, and analytical applications, you’ll feel right at home here.

What You’ll Do

• Design, build, and maintain data pipelines that ingest, transform, validate, and distribute weather, sensor, geospatial, and insurance data.
• Build reliable data infrastructure supporting catastrophe models, underwriting analytics, pricing, and post-storm analysis.
• Develop systems for processing large-scale time series and geospatial datasets efficiently and reproducibly.
• Work with engineers, data scientists, and product teams to define data requirements and turn them into scalable technical solutions.
• Improve the reliability, observability, quality, and performance of our data pipelines and storage systems.
• Build tools and services that make complex datasets accessible to internal users, customers, and downstream applications.
• Help establish standards for data quality, testing, documentation, lineage, and reproducibility.
• Use modern AI-assisted development tools, such as Claude Code or similar tools, to accelerate development, debugging, and problem-solving while applying sound engineering judgment and independently validating the results.

Your Day Might Include

• Building a pipeline to process millions of observations from Understory’s proprietary weather stations and make them available for downstream analysis and modeling.
• Designing workflows to transform raw weather observations into geospatial datasets used in catastrophe risk models.
• Optimizing queries and data processing workflows across cloud storage and distributed data systems.
• Investigating a data-quality issue, tracing it through an ingestion pipeline, and implementing safeguards to prevent it from recurring.
• Partnering with a Data Scientist to productionize a new model input or analytical workflow.
• Building APIs, services, or internal tools that allow underwriting and business teams to interact with risk data.
• Monitoring production data pipelines and improving their reliability as data volumes and use cases grow.

What You Bring

• 3+ years of experience in data engineering, software engineering, analytics engineering, or a related field.
• Strong Python and SQL skills, with experience building production-quality data pipelines.
• Experience working with large-scale time series, geospatial, or other complex datasets.
• Experience with cloud-based data infrastructure and storage, particularly S3 or similar object storage.
• Familiarity with distributed data systems, databases, and data processing frameworks.
• Strong understanding of data modeling, ETL/ELT patterns, testing, and data quality.
• Experience working with APIs, services, or other software systems that consume and produce data.
• A generalist mindset. You’re comfortable moving between infrastructure, application code, data modeling, and debugging.
• Strong communication skills and the ability to work effectively with data scientists, engineers, product teams, and business stakeholders.
• Comfort using AI-assisted development tools as a productivity aid. You know when and how to use tools like Claude Code or similar technologies to move quickly, while retaining the ability to reason independently, validate outputs, and take ownership of the code and systems you ship.

Nice to Have

• Experience with geospatial data and technologies such as GeoPandas, xarray, rasterio, PostGIS, or similar tools.
• Experience with Cassandra, S3, or other distributed/cloud-based data systems.
• Familiarity with Docker, Kubernetes, or container-based workflows.
• Experience with orchestration tools and production data pipelines.
• Experience working with weather, climate, catastrophe modeling, insurance, or other risk-related datasets.
• Experience building data infrastructure that supports machine learning or statistical modeling.
• Familiarity with observability, CI/CD, infrastructure-as-code, or cloud-native engineering practices.

What We Offer

We offer competitive compensation, full benefits, and the chance to do mission-driven work with tangible, real-world impact. You’ll be part of a small, collaborative team that’s helping redefine weather data and risk analytics in the insurance space.