Research Data Engineer

DRC is seeking a Research Data Engineer to support the organization and integration of research, financial, and business development data. The role focuses on reconciling historical and current datasets, building documented data pipelines and API integrations, applying data governance, and monitoring data quality in a backend-focused environment.

Responsibilities:

• Design how our historical and current data fit together, including resolving inconsistencies from changed formats, renamed fields, or different collection methods over time
• Build a clear, well-documented data dictionary and provenance record — where data came from, how it's changed, and what it means today
• Build data pipelines (ETL/ELT) to bring data in from internal tools and external vendors on a reliable schedule
• Build and maintain API connections between our systems and third-party software (CRM, accounting/finance tools, research platforms, and similar)
• Apply sensible data governance and access controls for sensitive financial and confidential records
• Monitor data quality over time and flag issues before they affect analysis
• Partner with IT/outside contractors as needed on cloud infrastructure and deployment, especially early on

Requirements:

• A degree or coursework in Library Science, Information Science, or a related field (MLIS or equivalent experience)
• Demonstrated programming experience — coursework, personal projects, prior job, or research work involving Python and/or SQL
• Strong understanding of metadata, taxonomy, data provenance, and information organization principles
• Comfort learning new technical tools and systems independently
• Excellent documentation and communication skills — this role sits between technical and non-technical colleagues
• Bachelor's degree required
• graduate degree in library/information science strongly preferred
• Experience with research data management, digital scholarship, or data curation in an academic or research library setting
• Familiarity with cloud data platforms (Snowflake, BigQuery, AWS, or similar)
• Exposure to ETL/orchestration tools (Airflow, dbt) or API development
• Experience with survey, longitudinal, or panel data specifically