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# Data Quality Specialist

- **Company:** Milan Laser Hair Removal
- **Location:** Omaha, NE, United States
- **Contract:** Permanent contract
- **Skills:** Sql Data Warehouse, Data Analysis, Databases, Information Engineering, Database Testing, Python (Programming Language), Scrum Methodology, DataOps, SQL Databases, Scripting, Snowflake, Information Technology
- **Published:** August 20, 2026
- **Apply:** http://milanlaser.hrmdirect.com/employment/job-opening.php?req=3791562&req_loc=1437241&&

## About the Role

* High school diploma or equivalent (e.g., GED) required
 * Bachelor's degree in information technology, computer science, statistics, or a related field OR equivalent combination of education and experience combined (one year of experience equals one year of education) required
 * One year of experience working with data quality, data analysis, quality assurance, or database systems required
 * Three years of Structured Query Language (SQL) experience required
 * Understanding of data quality concepts such as accuracy, completeness, consistency, and timeliness required
 * Collaborative, team-first mentality with the ability to communicate technical issues to both business and technical audiences required
 * Effective written and verbal communication skills required
 * Experience with Snowflake or another cloud data warehouse required
 * Python or other scripting language experience preferred
 * Experience with data observability or data testing tools (e.g., Snowflake Data Metric Functions, Great Expectations, Elementary/dbt tests) preferred
 * Experience working in an Agile/Scrum project development environment preferred

## Description

Champion data quality standards and practices across Milan's data platform. Build and maintain ongoing, automated checks that catch data issues early rather than relying only on upfront validation. Partner with data engineers, the business intelligence team, and business stakeholders to embed data quality into the way Milan builds and maintains its data. Investigate data quality issues, identify root causes, and drive improvements that prevent problems from recurring., * Data Quality Standards & Frameworks: Develop and maintain Milan's data quality framework, standards, and documentation. Define what accurate, complete, and reliable data looks like for critical datasets in partnership with data engineering and business intelligence team members. Champion adoption of data quality standards and practices across the data team.

 * Continuous Monitoring & Testing: Build and maintain ongoing, automated data quality checks using Snowflake Data Metric Functions and related tooling. Monitor pipelines and datasets for accuracy, completeness, and consistency on a continuous basis rather than relying solely on upfront validation. Perform upfront testing as needed to ensure that new pipelines match baseline data. Identify and escalate data quality issues before they reach business users.
 * Root Cause Analysis & Remediation: Investigate data quality issues to identify root cause, partnering with data engineers to implement fixes. Track data quality issues and trends over time to identify systemic problems. Recommend process, pipeline, or governance improvements to prevent recurring issues.
 * Collaboration & Advocacy: Partner with data engineers, business intelligence team members, and business stakeholders to embed data quality checks into new pipelines and reports as they are built. Communicate data quality findings, risks, and improvements clearly to both technical and business audiences. Support team-wide accountability for data quality by sharing standards, documentation, and training.


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