Data Engineer - Climate Accountability Lab

The University of Miami's Climate Accountability Lab seeks a full-time Data Engineer to support climate research. Responsibilities include designing ETL pipelines, developing data architectures, and applying machine learning and natural language processing techniques. Candidates should have a relevant degree and proficiency in Python/R, with experience in data engineering and misinformation research. The role involves collaboration with academic institutions and managing outreach efforts.
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The Climate Accountability Lab headed by Prof. Geoffrey Supran at the University of Miami’s Rosenstiel School of Marine, Atmospheric, and Earth Science has a funded opening for a full-time Data Engineer (Data Engineer 1) to support research across multiple research domains, from tracking climate discourses on social media to investigating fossil fuel industry ties to higher education. This position will involve designing ETL pipelines, supporting machine learning and natural language processing applications, and integrating diverse data sources for computational and qualitative research. The ideal candidate will have strong data science skills and experience with system development, data infrastructure, and AI-driven analysis.

Key Responsibilities

Data Science & Engineering
• Collect and process data using web scraping, social media scraping, and database integration.
• Design and build automated ETL pipelines to extract, transform, and load (ETL) data from various sources into research databases.
• Develop and maintain data architectures that support large-scale text analysis and machine learning applications.
• Implement data pipelines for machine learning, natural language processing, and large language model applications, including topic modeling and supervised learning.
• Publish processed data to web interfaces, databases, and peer-reviewed publications.

Research & Theoretical Analysis
• Apply social science theories to analyze corporate influence in climate-related messaging.
• Develop new theoretical models to better understand patterns of climate change misinformation and propaganda.
• Write and publish peer-reviewed articles based on data-driven insights.

Collaboration & System Development
• Work with academic institutions and non-profit organizations to develop shared data infrastructure.
• Participate in requirements gathering, system design, and data modeling to align engineering solutions with research needs.
• Research and implement new architectures to improve data management, storage, and computational efficiency.

Administrative & Outreach
• Write and contribute to grant proposals.
• Manage the lab’s social media presence to share findings and amplify public engagement.
• Ensure compliance with university data security policies and best practices.

Required qualifications
• Bachelor’s or Master’s degree in fields including but not limited to Computational Social Science, Data Science, Computer Science, Political Science, Communication Science, quantitative History of Science, or Sociology.
• Proficiency in Python and/or R for data engineering, ML, and NLP applications.
• Experience designing ETL pipelines and working with SQL/NoSQL databases.
• Familiarity with ML/NLP techniques, cloud platforms (AWS, GCP, Azure, etc.), and distributed computing.
• Demonstrated knowledge of misinformation research.
• Excellent written and verbal communication skills.

Preferred qualifications
• Strong publication record in relevant fields.
• Familiarity with climate change misinformation, obstructionism, politics, and/or communication.
• Experience working with interdisciplinary research teams or in cross-sector collaborations (for example between academia and NGOs)

The University of Miami is an Equal Opportunity Employer - Females/Minorities/Protected Veterans/Individuals with Disabilities are encouraged to apply. Applicants and employees are protected from discrimination based on certain categories protected by Federal law. Click here for additional information.

Job Status:
Full time

Employee Type:
Staff

Pay Grade:
A11