Data Engineer
Listed on 2026-03-01
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IT/Tech
Data Engineer, Data Analyst, Data Science Manager, Data Security
About the Organization
The National Resident Matching Program® (NRMP®) is a private, not-for-profit organization established in 1952 to provide a uniform date of appointment to positions in graduate medical education (GME) in the United States. The NRMP is not an application processing service; rather, it provides an impartial venue for matching applicants’ and programs’ preferences for each other consistently. The NRMP conducts the annual Main Residency Match encompassing 40,000 applicants for more than 25,000 positions in core residencies as well as Matches for fellowship positions in 55 subspecialties through its Specialties Matching Service®.
PositionSummary
NRMP is looking to fill the full-time position of data engineer. The data engineer will play a pivotal role in operationalizing the data and analytics initiatives for NRMP. The data engineer will be building, managing, integrating, and optimizing reusable data pipelines and moving these data pipelines effectively into production for key data and analytics consumers - business/data analysts, research analysts, executives, and internal departments - that need curated data sets for data and analytics use cases.
The data engineer must support compliance with data governance and data security and privacy requirements to enable faster data access, integrated data reuse and vastly improved time-to-solution for NRMP data and analytics initiatives. The data engineer will be measured on their ability and agility to integrate analytics results with NRMP’s business processes.
This role will be the key interface in operationalizing data and analytics on behalf of organizational outcomes and will require both creative and collaborative aspects- working with both IT and the wider business. The data engineer will also be tasked with working with key business stakeholders, IT experts and subject-matter experts to plan and deliver optimal analytics solutions.
Essential Duties and Responsibilities- Build data pipelines:
Managed data pipelines consist of a series of stages through which data flows from data sources of acquisition to integration to consumption for specific use cases. These data pipelines must be created, maintained, and optimized as workloads move from development to production for specific use cases. Architecting, creating, and maintaining data pipelines will be the primary responsibility of the data engineer. - Drive Automation through effective metadata management:
The data engineer will be responsible for using innovative and modern tools, techniques, and architectures to partially or completely automate the most-common, repeatable and tedious data preparation and integration tasks in order to minimize manual and error-prone processes and improve productivity. The data engineer will also need to assist with renovating the data management infrastructure to drive automation in data integration and management. - This will include (but not be limited to):
- Using modern data preparation, integration and metadata management tools and techniques.
- Tracking data consumption patterns.
- Monitoring schema changes.
- Recommending and automating — existing and future integration flows.
- Collaborate across departments:
The newly hired data engineer will need strong collaboration skills to work with varied stakeholders within the organization. In particular, the data engineer will work in close relationship with research teams and with data analysts in refining their data requirements for various data and analytics initiatives and their data consumption requirements. - Become a data and analytics evangelist:
The data engineer will be considered a blend of data and analytics “evangelist,” “data guru” and “fixer.” This role will promote the available data and analytics capabilities and expertise to business unit leaders and educate them in leveraging these capabilities in achieving their business goals.
- At least 6-8 years or more of work experience in data management disciplines including data integration, modeling, optimization, and data quality, and/or other areas directly relevant to data engineering responsibilities and…
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