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Data Engineer – Scientific Document and Data Pipelines

Responsible for designing, developing, and implementing automated scientific data extraction and standardization solutions supporting the National Institute of Standards and Technology (NIST) CHIPS METIS initiative. Develops advanced Python-based pipelines to extract figures, charts, and tables from scientific documents and transform non-standard research data into structured, machine-readable formats. Implements Optical Character Recognition (OCR), document layout analysis, data normalization, and schema validation capabilities. Integrates data processing tools and microservices into existing web applications and research data repositories. Works closely with government scientists and technical stakeholders to ensure data accuracy, accessibility, and interoperability. Develops technical documentation, maintains source code repositories, and supports the delivery of reliable, reusable software solutions.

Responsibilities / Tasks

Design and implement automated OCR and document layout analysis pipelines to extract scientific figures, charts, and tables from PDF and Microsoft Word documents.
Develop algorithms to identify and extract figure captions, axis labels, measurement units, and associated scientific metadata.
Implement table extraction capabilities that preserve row and column structures, headers, and data relationships.
Integrate extracted metadata with established controlled vocabularies and ontologies within the Controlled Vocabulary Curation System (CVCS).
Develop and enhance user interfaces that enable scientific domain experts to validate extracted content and correct OCR errors.
Develop Python-based parser libraries to convert proprietary instrument outputs, legacy spreadsheets, and non-standard text files into standardized CSV formats.
Implement automated data normalization, schema validation, and quality assurance procedures to ensure compliance with METIS data schemas.
Develop heuristic data-cleaning capabilities to address missing values, merged cells, inconsistent headers, and extraneous metadata.
Integrate extraction and standardization tools as microservices or software modules within the existing CVCS web application architecture.
Develop data export capabilities to support integration with the METIS Discovery Portal.
Prepare design documentation, implementation guides, API specifications, and deployment procedures.
Develop, test, and maintain documented source code within GitHub repositories, including code intended for public release.
Prepare and deliver technical demonstrations of extraction workflows, data conversion capabilities, and system enhancements.
Monitor and report technical performance metrics, including document processing volumes, OCR accuracy, data conversion success rates, and system integration status.
Collaborate with government scientists, developers, and technical stakeholders to identify requirements, troubleshoot technical issues, and improve system functionality.

Required Skills

Bachelor's degree (four-year) in Computer Science, Data Engineering, or a related field.