Data Verification Specialist

Iowa State University•Ames, IA

4d•Hybrid

About The Position

Iowa’s Integrated Data System for Decision-Making (I2D2) seeks an exceptionally detail-oriented Data Verification Specialist (Data Analyst II) to join our team. Reporting to the Data and Analytics Manager, this role serves as a senior individual contributor with primary responsibility for executing the technical components of our data lifecycle within a secure, offline environment. In this position, you will lead efforts to manually review, restructure, clean, standardize, and document complex datasets across multiple platforms. In addition to data maintenance, you will transform and compute variables and perform final data preparation to ensure high-quality data assets ready for downstream analytical use. This is a technical Human-centric data management role focused on ensuring the foundational integrity and health of data, rather than generating business insights. The ideal candidate is a self-directed meticulous problem solver who thrives on precision, can manage complex data tasks with minimal supervision, and has a manual reconciliation focus. This is not a data engineering or business analytics role. The interdisciplinary team at Iowa’s Integrated Data System for Decision-Making (I2D2) includes faculty, professional staff, postdoctoral researchers, and graduate and undergraduate students who collaborate closely with government agencies and statewide stakeholders. Together, they provide actionable data to inform decision-making and strengthen coordination and effectiveness for Iowa families. I2D2 transforms state administrative data to answer critical questions about system performance and to strengthen coordination and effectiveness for Iowa families. We are a motivated, action-oriented team that values strong communication and collaboration. Our work environment blends virtual and in-person engagement, fostering flexibility and supporting a healthy work–life balance. We prioritize mutual support, celebrate one another’s successes, and intentionally leverage each team member’s unique strengths.

Requirements

Bachelor’s degree and 3 years of related experience
Candidates must be legally authorized to work in the U.S. on an ongoing basis without sponsorship.

Nice To Haves

Bachelor’s degree in a quantitative field such as Social Sciences, Statistics, Information Management, or a related discipline.
Professional experience managing large-scale complex longitudinal and/or administrative datasets, including data quality assessment, validation, cleaning, documentation, and overall data management.
Strong professional experience managing data with R/RStudio, particularly data.table and dtplyr, and proficiency in conducting data management and descriptive analyses in Stata, SPSS, and Excel.
Experience or familiarity with recursive schema perturbation and iterative schema dissolution methods applied to flat-file data architectures.
Exceptional technical writing and metadata management skills, including maintaining data manuals and codebooks
Familiarity with project documentation and management tools including Microsoft Loop and Smartsheet.

Responsibilities

Implement data quality frameworks and validation criteria across data domains to identify and resolve or document anomalies and inconsistencies.
Develop and apply standardized data definitions ensuring adherence to strict internal schemas and naming conventions across diverse data systems and products.
Create new derived variables, recode existing data elements, and compute complex indicators to prepare datasets for statistical analysis, ensuring all transformations are logically consistent and accurate.
Build and maintain rigorous data manuals, data catalogs, technical reports, and metadata documents in Microsoft Loop.
Document all restructuring processes, cleaning logic, computed variable formulas, validation checks and version histories.
Create and maintain comprehensive data dictionaries, validation checklists, and process documentation summaries of datasets constructed for analytic project use.
Conduct preliminary data profiling, including frequencies, means, and other descriptive statistics to identify anomalies and provide initial data insights to the Data and Analytics Team and project analytic teams.
Architect and export "analysis-ready" datasets tailored to specific user requests, ensuring all merges, appends, and variable derivations are accurate, verified, and documented.
Operate with high autonomy across diverse analytic environments, including R, Stata, SPSS, and Excel. You must be comfortable performing complex data manipulations and ensuring consistent results when moving data between these tools.