Data Engineer: Lab Data Pipelines & AI Tools

GroundWork Renewables; Albuquerque (NM); On-site; USD 90,000 - 130,000; Full time

Benefits offered by this job

GroundWork is a Certified B Corp

Job summary

GroundWork Renewables seeks a Data Engineer to design, build, and maintain internal data infrastructure and pipelines. You will enable lab teams to access high-integrity datasets for analysis, reporting, and operations while ensuring compliance with regulatory standards.

The role collaborates with engineering, laboratory, and operations teams to deliver robust data solutions, leverage AI-assisted tools, and maintain data governance across the lab environment.

Qualifications

• Minimum of 3 years of data engineering and pipeline experience in lab/renewables.
• Experience designing production data pipelines and infrastructure.
• PV testing or solar energy lab environment is a plus.
• BS degree in CS/SE/IS; advanced degree or certs preferred.

Responsibilities

• Design, implement, and maintain relational and time-series databases for lab data.
• Develop and manage ETL/ELT pipelines ingesting data from IoT sensors and instruments.
• Build internal data tools and dashboards using modern web frameworks.
• Apply AI-assisted development tools to accelerate delivery while ensuring auditability.
• Maintain QA/QC workflows and data governance per regulatory standards.
• Architect schemas for performance, scalability, and accessibility.

Skills

SQL & Relational DBs

Python

ETL/ELT Pipelines

Web Dashboards

QA/QC for data

Communication

Git / Cloud

AI-assisted tools

LIMS familiarity

Education

BSc in CS/SE/IS

Tools

Streamlit

FastAPI

React

SQLAlchemy

Airflow

Docker

GitHub Copilot

Cursor/Claude Code

Job description

GroundWork Renewables seeks a Data Engineer to design, build, and maintain internal data infrastructure and pipelines. You will enable lab teams to access high-integrity datasets for analysis, reporting, and operations while ensuring compliance with regulatory standards.

The role collaborates with engineering, laboratory, and operations teams to deliver robust data solutions, leverage AI-assisted tools, and maintain data governance across the lab environment.