Data Engineer

Location:

Albuquerque, NM

Company Overview Ground Work Renewables is the solar industry's trusted full-stack performance partner. A Certified B Corporation and ISO/IEC 17025-accredited testing provider, we deliver precise meteorological data and PV module insights — helping developers, EPCs, and asset owners reduce risk, improve forecasting, and maximize value throughout the project lifecycle. Our services have enabled 1,000+ solar measurement campaigns and helped project developers secure billions in financing by reducing uncertainty with trusted resource data.

Backed by a recent strategic investment that positions us for accelerated growth, Ground Work is expanding its U.S. operations — and we are hiring a Data Engineer to power this next phase of growth.

Ground Work seeks a Data Engineer to design, build, and maintain the internal data infrastructure, pipelines, and tools that make laboratory measurement data reliably available to the lab team. This role sits at the intersection of database engineering, data pipeline development, internal tool and application development, and data quality assurance, ensuring that high-integrity datasets are ingested, managed, and surfaced through modern, scalable systems that support the lab’s own analysis and operations.

The ideal candidate combines strong technical skills in database engineering, data modeling, and software development with familiarity with solar energy measurement, accredited laboratory environments, and regulatory data standards. As a Data Engineer, you will work closely with Ground Work’s engineering, laboratory, and operations teams to prioritize, design, and deliver robust data pipelines and systems that meet the needs of the laboratory’s internal users.

You will leverage AI-assisted development tools to accelerate the delivery of internal tools and data pipelines, while maintaining the rigor and traceability required in a laboratory and regulatory context. This role requires a technically versatile individual who can work across disciplines to drive data reliability, accessibility, and operational excellence.

• Technical Subject Matter Expertise
  • Design, implement, and maintain relational and time-series databases for lab instrument data, environmental measurements, and operational records.
  • Develop and manage ETL/ELT pipelines to ingest, transform, and store data from IoT sensors, measurement hardware, and remote sensing platforms.
  • Build and deploy internal data access tools and applications using modern frameworks (e.g., Streamlit, FastAPI, React, or similar) to enable lab staff to query, visualize, and export lab data.
  • Apply AI-assisted development tools (e.g., Git Hub Copilot, Cursor, Claude) to accelerate software delivery while maintaining code quality and auditability appropriate for a laboratory environment.
• Data Quality Assurance & Control
  • Develop and enforce QA/QC protocols to validate incoming data from lab instruments and field sensors in accordance with applicable regulatory and accreditation standards (e.g., ISO 17025 or similar).
  • Implement automated checks, flagging routines, statistical validation, and audit trails to detect anomalies, missing data, and calibration drift.
  • Maintain defensible data records that satisfy chain-of-custody and traceability requirements.
  • Ensure data integrity from acquisition through delivery to downstream consumers.
• Database Architecture & Optimization
  • Architect and optimize database schemas for performance, scalability, and ease of access.
  • Evaluate and recommend appropriate database technologies (SQL, No
    
    SQL, time-series) based on data volume, query patterns, and the lab’s analysis and reporting requirements.
• Stakeholder Collaboration
  • Partner with lab engineers, metrology staff, and operations to understand data access requirements and translate them into technical solutions.
  • Serve as the primary point of contact for the lab’s internal data availability and reporting needs.
• Internal Tool & Application Development
  • Design and build internal data access tools, dashboards, and reporting interfaces using modern full‑stack frameworks (e.g., React, FastAPI, Streamlit, Plotly…