Senior Manager, Healthcare Data Engineering

American Society Of Hematology•Washington, DC

About The Position

The Senior Manager, Healthcare Data Engineering designs, develops, implements, and deploys scalable clinical data management solutions that can adequately handle research needs. This role is responsible for building a high quality, robust data infrastructure to support large-scale clinical datasets storage, processing, access, and security. Additionally, the Senior Manager, Healthcare Data Engineering will drive real-world evidence (RWE) data-driven insights and innovation by leveraging advanced software techniques and data engineering expertise. They will develop complex clinical datasets for extracting meaningful insights, support predictive modeling, and enhance research and key business decisions through optimized clinical data management solutions.

Requirements

Master’s degree in computer science, Data Science, Bioinformatics, or a related field; or a Bachelor’s degree with directly relevant experience.
7+ years of hands-on relevant experience in healthcare and clinical data analysis, database administration, data engineering, or software engineering.
Strong knowledge of healthcare terminologies, ICD-10, SNOMED, RxNorm, CPT/HCPCS, LOINC, and Athena.
Familiarity with the HL7 FHIR, USCDI standards, and OHDSI OMOP CDM database and terminology standards
Experience with data frameworks and cloud platforms (e.g., AWS, Azure, GCP).
Experience with data visualization tools (e.g., Tableau, Power BI).
Familiarity with cloud platforms data warehousing and data lake architectures.
Experience with database management and administration (e.g., Oracle, Microsoft SQL Server).
Working knowledge of relational database design and analysis, including expert knowledge of developing queries using SQL, R, Python, SAS, etc.
Experience managing data for health-related organizations, including FDA, NIH, and the life science industry.
Strong problem-solving and analytical skills and able to tackle complex data challenges.
Excellent verbal and written communication skills, to convey technical concepts to diverse audiences.
Strong collaboration skills to engage with stakeholders across disciplines.
Ability to work both independently and in a team environment.
Requires ability to speak audibly and listen actively.
Ability to use computers telephones and other office equipment
Will require minimal air travel.

Nice To Haves

Work experience in the hematology field preferred.
Work experience with real world data clinical registries.
Experience with Salesforce contact management database software is a plus.
Prior experience in a not-for-profit environment and working with people in various healthcare professions.

Responsibilities

Collaborate with vendors to design, develop, and implement scalable data pipelines to ensure efficient ingestion, transformation, storage, and retrieval of structured and unstructured healthcare data from various sources.
Develop and optimize OMOP Common Data Model (OMOP CDM) and data dictionaries to support research and clinical applications, ensuring seamless integration of high-throughput clinical datasets, as developed by the Observational Health Data Sciences and Informatics (OHDSI) community.
Implement ETL processes to prepare datasets for statistical modeling, predictive analytics, and data science.
Automate and enhance data processing workflows to improve the efficiency and reproducibility of data transformations.
Optimize performance for large-scale data retrieval, indexing, and queries to ensure reliable access to dataset
Monitor and enhance security, logging, and auditing mechanisms to ensure regulatory compliance for clinical data
Develop metadata management systems to enhance data discoverability and accessibility for researchers.
Stay informed on emerging data engineering, informatics, and machine learning technologies, incorporating best practices into new data strategies and initiatives.
Explore and implement innovative data-driven solutions and industry standards (GitHub, Bugzilla).
Conduct data cleaning, validation, and quality assurance, including exploratory data analysis (EDA), to identify and resolve data quality and integrity issues.
Develop automated data quality monitoring pipeline to track completeness, consistency, and accuracy of incoming data mart clinical datasets.
Collaborate and work closely with provider sites and data vendors to ensure compliance with data governance standards and resolve discrepancies.
Establish data validation rules, anomaly detection methods, and quality control metrics to maintain high-quality clinical data for research and analytics.
Document data lineage, transformations, and quality assessments to ensure reproducibility.
Support research and quality assurance projects utilizing large healthcare databases using knowledge of relational databases and medical coding terminologies including, but not limited to CPT, ICD-10, SNOMED, RxNorm, CPT/HCPCS, and LOINC.
Support ASH RC multicenter research studies by administrating and developing REDCap Cloud research databases, and datasets that support reporting and analytics as needed.
Manage Data Hub data delivery timelines to ensure deliverables align with project milestones, priority targets, and work planned with vendors and other stakeholders.
Communicate and clarify complex reports and analyses with the appropriate project stakeholders.
Support all study/projects by utilizing a working knowledge of the FHIR interoperability and OMOP standards.
Submit to the Senior Director of the Data Hub for review and approval, all research data-related timelines and projected level of effort.
Work closely with cross-functional ASH RC teams/staff, including, ASH RC contractors, external consultants, research scientists, medical doctors and researchers, data scientists, bioinformaticians, and software engineers, to develop scalable and reusable clinical data solutions to achieve shared goals.
Monitor projects, track progress, and report on deliverables to meet program milestones, targets, and goals.
Guide study data management best practices.
Foster a data-driven culture within the organization.