Data Engineer

Crux is the capital platform for the clean economy. The company is hiring a Data Engineer to build and maintain the data foundation behind its Market Intelligence practice, including pipelines, automation, and reporting support for research, publications, and client deliverables. The role focuses on making survey, pricing, forecasting, and buyer participation data clean, reliable, and reusable across the organization.

Responsibilities:

• Own the Market Intelligence data foundation. Build and maintain the pipelines our market intelligence work runs on: survey responses, pricing series, volume and capacity forecasts, buyer participation. Extraction, transformation, scheduling, monitoring, the whole path
• Automate our survey processes end to end. Ingesting, cleaning, joining - a lot of this is ad hoc today. You will make it a system, so that scaling our survey program does not mean scaling hours of manual cleanup
• Get data where it needs to be. Supporting the reporting layer so that findings are visible and plugged into the tools, dashboards, and publications that depend on them, without an analyst stitching it together each time
• Turn recurring questions into repeatable queries. As bespoke research scales, you will be the person who will help productize
• Firm up Crux's data practices. With the data foundation in place, you’ll help move it onto firmer ground: validating inputs, documenting assumptions, version control, reproducible outputs, finding and driving additional use cases across other teams

Requirements:

• • 3+ years of experience building and maintaining production data pipelines in high-growth, fast-paced environments
• • Fluent in Python and SQL, with the ability to write, test, and debug production code and complex queries
• • Proficient in cloud data warehousing, including schema design, incremental data loading, query optimization, and cost management
• • Experience with extraction, transformation, loading, scheduling, and monitoring
• • Knowledge of established data-quality practices, including input validation, automated testing, monitoring, documentation, version control, and reproducible outputs
• • Skilled at translating business or research needs into clearly scoped technical requirements, deliverables, and implementation plans
• • Demonstrated ability to communicate technical tradeoffs, project status, data limitations, and delivery risks to technical and nontechnical stakeholders
• • Hands-on experience with AI-enabled tools supporting software development, data analysis, testing, documentation, or workflow automation
• • Sound technical judgment when evaluating datasets and engineering approaches against downstream reporting, research, or product requirements
• • Experience working with energy, finance, transaction, or market intelligence data
• • Exposure to research, forecasting, survey analysis, or data-intensive publications
• • Hands-on involvement in developing or supporting analytical or statistical models
• • Knowledge of model testing and data-validation practices, including documenting assumptions and handling missing values and outliers
• • Familiarity with data visualization, business intelligence, or reporting tools