Research Data Analyst

SUMMARY:

Reporting to the Manager, Research Data, the Research Data Analyst is the primary analyst for the build out of Brown University Health's research data ecosystem: a secure cloud environment that integrates, curates, and validates clinical and research data, supports cohort identification, and provides governed, standardized access for investigators and external partners.

The Analyst owns the build-out of the ontologies, vocabularies, and metadata that make research data understandable and usable. The primary focus is transforming ingested data into i2b2 or a similar common data model, mapping local source data to standard clinical terminologies, and documenting what each data element means, where it came from, and how it was derived. Over time, the Analyst may support extending the environment to additional data models such as OMOP.

This is a hands-on role with a high degree of ownership. The Analyst is expected to drive much of their own analytic work, working within the processes, roadmap, and vision set by the Manager, Research Data, the Director, Data Architecture, and the Chief Data and Analytics Officer. The Analyst works closely with investigators, the research facilitation group, and the technical staff building out the research data ecosystem.

PRINCIPAL DUTIES AND RESPONSIBILITIES:

The Research Data Analyst will be responsible for the ontology, vocabulary, metadata, and analytic work that turns ingested source data into curated, research-ready data. Responsibilities include:

• Own the design, build-out, and maintenance of i2b2 (or similar common data model) ontologies, including hierarchies, local concepts, synonyms, code crosswalks, and terminology version updates.
• Map local, non-standard source codes (for example, hospital-specific billing codes, custom lab panels, and flowsheet rows) to standard clinical terminologies, including SNOMED CT, ICD-10-CM, LOINC, RxNorm, and CPT.
• Develop and maintain the metadata needed to understand data ingested into the research data environment, including data dictionaries, source-to-target mappings, mapping provenance, data lineage, and plain-language definitions for researchers.
• Write, test, and optimize SQL transformations that move ingested source data into i2b2 fact and dimension tables (or a similar common data model), working within the ingest pipelines operated by Data Architecture & Analytics.
• Validate the completeness, accuracy, and longitudinal consistency of research data through data profiling and quality checks, and investigate and resolve data-quality issues with source system owners.
• Apply data governance requirements in daily work, including confirming IRB approvals and data use agreements, minimum-necessary data delivery, de-identified and limited data set standards, and documentation needed to support accounting of disclosures.
• Identify recurring requests and common definitions that can become reusable data products, standard concept sets, or self-service capabilities, and recommend them to the Manager, Research Data for prioritization within the team roadmap.
• Perform other duties as assigned.

EDUCATION:

Bachelor's degree in Biomedical or Clinical Informatics, Health Information Management, Computer Science, Data Science, Epidemiology, Biostatistics, Public Health, or a related quantitative field required.

Master's degree in Biomedical Informatics, Epidemiology, Biostatistics, Public Health, Data Science, or a related field preferred.

Equivalent combinations of education and relevant professional experience may be considered in accordance with Brown University Health requirements.

EXPERIENCE:

A minimum of 4 years of hands-on data analysis experience in an academic medical center, healthcare organization, biomedical research, or life sciences environment, including direct work with clinical or electronic health record data.

Required Qualifications

• Advanced SQL skills, including complex multi-table queries, transformations, views, and performance tuning on large clinical data sets in platforms such as Snowflake, SQL Server, PostgreSQL, or Oracle.
• Strong understanding of clinical terminologies such as SNOMED CT, ICD-10-CM, LOINC, RxNorm, CPT, and UMLS, including hierarchies, mappings, and versioning.
• Experience working with electronic health record data and its complexities for research or analytics.
• Demonstrated ability to own analytic work from requirements through delivery with limited day-to-day supervision.
• Strong attention to detail and documentation habits, with the ability to explain data definitions and limitations clearly to researchers and non-technical audiences.

Preferred Qualifications

• Hands-on experience with i2b2 or OMOP, including loading data into the model, ontology or vocabulary build-out, and cohort discovery tools such as the i2b2 web client or ATLAS (highly preferred).
• Prior experience as a researcher, research analyst, or research coordinator on funded studies, including familiarity with the research lifecycle from protocol through publication.
• Working knowledge of research data frameworks, including common data models, cohort discovery, FAIR data principles, and observational and retrospective research.
• Understanding of research data governance, including IRB processes, data use agreements, HIPAA de-identification, limited data sets, and access controls.
• Experience with Epic Clarity or Caboodle.
• Proficiency in Python or R for data parsing, profiling, and validation.
• Experience with modern cloud data platforms.

This position is grant funded for five years, with the intent to transition to additional funding thereafter.

SUPERVISORY RESPONSIBILITY:

None.

Pay Range:

EEO Statement:

Brown University Health is committed to providing equal employment opportunities and maintaining a work environment free from all forms of unlawful discrimination and harassment.

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From the posting on Brown University Health's job site. Read the original before you apply.