Data Integration Analyst II; Aplenc Lab

Children's Hospital of Philadelphia•Philadelphia, PA

2d•$101,300 - $129,100

About The Position

Dr. Richard Aplenc’s collaborative team at Children’s Hospital of Philadelphia is seeking a highly self-motivated Data Integration Analyst II. The team’s work aims to improve clinical outcomes for pediatric cancer patients through translational, large-scale clinical data initiatives. This role will contribute to the development and implementation of ExtractEHR+, an automated electronic health record (EHR) data extraction and curation toolkit designed to make high-quality clinical data more accessible, consistent, and useful for research, including in the Epic Cosmos environment. The analyst will independently develop integrated biomedical data resources and applications supporting data-intensive clinical and translational research. The role focuses on designing, improving, and supporting automated workflows to extract, transform, validate, and integrate EHR data from diverse clinical systems and sources. The analyst will collaborate with biomedical researchers, clinicians, informaticians, and technical teams; create data models, ETL procedures, and research applications; and manage small projects and subprojects in a highly matrixed research environment.

Requirements

Bachelor’s degree.
At least three years of database development or administration, data management, or related experience.
Intermediate proficiency in data integration and data architecture.
Intermediate proficiency in data modeling, ETL, and applications of highly dimensional data types, including genomics and observational clinical or human-subjects research data.
Intermediate proficiency in source code management, continuous integration, containerization, and automated testing tools and processes.
Substantial knowledge of programming languages including SQL, Python/Django, JavaScript/HTML, Java, and Scala.
Substantial knowledge of relational database management systems, including PostgreSQL, MySQL, and Oracle.
Substantial knowledge of biomedical or healthcare data models and standards, including HL7, ICD, CPT, and SNOMED.
Excellent verbal and written communication skills.
Strong time management, organizational, presentation, and project management skills.

Nice To Haves

Master’s degree in computer or information science, informatics, biomedical engineering, biological science, or a related field.
At least five years of database development or administration, data management, or related experience within a biomedical science or healthcare environment.

Responsibilities

Analyze clinical care, clinical research, laboratory, and EHR documentation workflows to deliver customized data-management, integration, reporting, and analytic solutions.
Support the development and expansion of ExtractEHR+, including automated extraction of structured and unstructured EHR data, normalization, quality assurance, provenance tracking, and integration with research databases and analytic platforms.
Develop scalable data pipelines and data models that transform raw EHR and related biomedical data into research-ready datasets.
Collaborate with clinicians, researchers, informaticians, and technical teams to identify priority EHR data elements, define extraction requirements, and validate extracted data.
Improve the quality, consistency, utility, and accessibility of clinical and scientific data throughout its lifecycle, especially real-world EHR data.
Build consensus and buy-in for complex data-integration initiatives by defining milestones and delivering iterative data-management, database, application, and automated-extraction products.
Develop and apply best practices for biomedical data resources, including data modeling, interoperability, provenance, integration, quality control, governance, and patient privacy protection.
Work independently and with project teams in a highly matrixed research environment, interacting directly with clinicians and biomedical researchers.
Collaborate with biomedical researchers to identify and analyze scientific problems requiring integration of disparate, highly dimensional data, such as clinical and genomic data.
Acquire biomedical domain knowledge to understand integration requirements and optimize data resources and applications for scientific needs.
Create requirements for complex data-integration and application-development projects and translate them into deliverables.
Develop and implement data models that represent complex biomedical data types in usable and accessible schemas.
Write ETL procedures that combine and recombine biomedical data into useful formats.
Build and optimize scientific data-management, discovery, reporting, and analysis applications using off-the-shelf and custom tools.
Manage small projects and subprojects within larger initiatives, tracking and reporting tasks and deliverables against timelines.