Crux Climate
Data Engineer
About this role
Crux, a clean energy capital platform, seeks a Data Engineer to build and maintain production data pipelines for the Market Intelligence team. You'll automate survey processing, establish data quality practices, and create the foundational datasets that power market research, publications, and client deliverables.
What you'll do
- Design and maintain ETL pipelines for survey responses, pricing data, capacity forecasts, and market participation metrics
- Automate end-to-end survey ingestion, cleaning, and joining processes to eliminate manual data preparation
- Build reporting layers and dashboards that surface Market Intelligence findings across tools and publications
- Establish data validation, monitoring, documentation, and version control practices across the data foundation
- Convert recurring research questions into repeatable, productized queries and datasets
- Collaborate with analysts to translate business requirements into technical specifications and implementation plans
What they're looking for
- Python and SQL (production-level code writing, testing, debugging)
- Cloud data warehousing (schema design, incremental loading, query optimization, cost management)
- ETL/ELT tools, scheduling, and monitoring frameworks
- Data quality practices and automated testing
- Requirements translation and stakeholder communication
- AI-enabled development tools
- Version control and reproducible pipeline design
- Data modeling and warehousing architecture
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Crux Climate
Crux Climate builds LLM-powered products that modernize clean energy financing by automating traditionally manual processes. The company is hiring AI Product Engineers for full-stack development and GTM Engineers to build technical infrastructure for go-to-market operations.
- Website
- cruxclimate.com
Likely interview questions
- Walk us through a production data pipeline you built from scratch—what were the biggest technical challenges and how did you solve them?
- Tell us about a time you had to automate a manual, repetitive data process. How did you prioritize what to tackle first?