Data Integration Specialist (Multiple Openings, Temporary)

University of Washington•Seattle, WA

11d•Remote

About The Position

IHME has an outstanding opportunity for multiple fixed term Data Integration Specialists to join the Data Quality Impact Team (DQI). This role is responsible for leading system-level discovery and stewardship of IHME's data, leveraging the Global Health Data Exchange (GHDx), emerging technologies, business intelligence practices, and institutional collaboration to identify data availability patterns and gaps, secure critical datasets, run quality diagnostics that inform model inputs, annotate resources for discoverability, and ensure responsible use of restricted data. This role requires curiosity and proactive thinking, learning how research teams use data for modeling, understanding data flows, and exploring where and how data is used. The position moves beyond mechanical data processing toward judicious, strategic stewardship, systematizing workflows to free time for supporting teams in using the best data available. Candidates must embrace a “Yes AND” mindset—not only meeting requests but expanding possibilities by suggesting additional data sources and approaches to ensure broad, innovative thinking. IHME’s office is located in Seattle, Washington. This position is eligible to work fully remote in the US; work schedule required to overlap 50% of core business hours (9am–3pm PT). IHME is a grant-funded organization, and this position is contingent on project funding availability.

Requirements

Bachelor's degree in social sciences, engineering, computer science, or related field and four years of experience in data quality assessments or coding for data workflows using Python or R.
Demonstrated experience working with structured and unstructured data, including data cleaning, transformation, and annotation.
Basic coding skills in one or more languages (e.g., Python or R) and ability to generate, adapt, and validate code with full comprehension of its function and alignment to institutional standards.
Demonstrated ability using business intelligence approaches for developing summary reports or dashboards, modeling data relationships, and visualizing trends for operational and research needs.
Demonstrated ability and comfort using emergent technologies effectively.
Strong analytical, problem-solving, and communication skills; ability to assess input data quality and suitability for modeling, synthesize technical details into meaningful insights, and work collaboratively to ensure optimal data selection.
Demonstrated curiosity and adaptability; willingness to learn IHME systems and tools.
Ability to embrace a growth mindset, expand ideas, and encourage broad thinking to ensure IHME uses the best data available.
Demonstrated self-motivation and evidence of self-direction.
Ability to interpret summary data and derive insights from it that are relevant to the user base.
Agile written and oral communication skills with technical audiences, demonstrating diplomacy and understanding related to topics of data policies, expectations of data sharing, and different cultural contexts.

Responsibilities

Ensure that the widest spectrum of possibly relevant data is available to research teams for use in their analyses.
Ensure that all data sources can be easily and efficiently queried, retrieved, and used.
Assess and identify data sources by a range of summary characteristics—including geography, years, indicators of interest, and population covered—to inform their use and the seeking out of new sources.
Develop and maintain dashboards and standard reports that provide actionable insights into data availability, quality, and usage trends across IHME systems.
Publish recurring insight briefs and KPI/OKR summaries tied to data quality and operational performance (e.g., intake cycle time, restricted data compliance status, refresh cadences) to inform priorities.
Interpret and synthesize metadata into actionable insights on data quality and limitations to guide research prioritization, while leveraging AI-assisted discovery (e.g., alerting for decay/gaps, surfacing candidate datasets) to support evidence-based decision-making and proactive stewardship.
Drive novel approaches to data discovery and ingest; help systematize previously ad-hoc activities to improve efficiency and quality.
Use automated tools in combination with informed practice and individual knowledge to make data seeking and assessment as efficient and high-quality as possible.
Innovate with AI-driven technologies and heuristic methods to seek and assess data and to scale identification and annotation of records consistent with UW and IHME data governance, identification, and availability standards.
Advise on and automate data intake processes to ensure efficient, standardized onboarding of external datasets into IHME systems, reducing manual effort and improving compliance with institutional data governance.
Demonstrate an understanding of the key characteristics of internally secured data in contrast to what is known to be or potentially could be attainable.
Evaluate with research teams the relevant data landscape for specific analyses to inform priority data-seeking exercises and to address challenges.
Help identify new or non-traditional data sources that are not health-specific but could include data on indicators that can inform analyses.
Run and document quality diagnostics (e.g., completeness, timeliness, duplication, lineage) and produce summary reports to guide input acceptance decisions and downstream modeling considerations.
Routinely deploy efficient techniques to seek out and update newly available data sources in a timely and comprehensive manner.
Track and report on data source access through stages including external availability, assessment, categorizing, integration into databases for use by researchers, and source summaries to the public.
Develop and implement effective data-seeking and assessment protocols.
Interact with external data providers to secure access to data and resolve questions.
Engage with members of IHME's Collaborator Network—a group of 16,000+ individuals from across the world who contribute to analyses and publications—to facilitate access to newly available data sources and answer questions about them.
Share timely information across IHME related to data source availability, quality, and potential impact.
Ensure that any required data security measures and regulations about use are appropriately identified alongside individual data sources.
Help research teams learn about and apply data-seeking protocols and tools.
Provide feedback on data pipeline reliability and metadata capture from a user perspective to improve data discoverability and research readiness.