Data Quality Lead
New
J
JobgetherData Operations
Remote-first work environment with flexibility to work from home across most of the United States.Full-TimeLead
Salary not disclosed
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Job Details
- Experience
- 4+ years
- Required Skills
- SQLBusiness IntelligenceData AnalysisProcess improvement
Requirements
- 4+ years of experience in data quality, data operations, analytics, research, or a related discipline.
- Strong analytical, investigative, and problem-solving skills with experience performing root cause analysis.
- Proven ability to translate data findings into actionable recommendations and operational improvements.
- Proficiency with SQL, spreadsheets, and business intelligence or reporting tools.
- Experience designing and improving data processes, quality controls, and operational workflows.
- Familiarity with automation tools, AI-assisted workflows, or human-review systems.
- Strong written and verbal communication skills with the ability to collaborate effectively across technical and business teams.
- Ability to manage priorities in a fast-paced, growth-oriented environment while maintaining attention to detail.
- Experience leading, mentoring, or managing analysts is preferred.
- Familiarity with data pipelines, data collection processes, real estate technology, or related industries is a plus.
Responsibilities
- Lead investigations into data quality issues, identify root causes, and collaborate with Product and Engineering teams to implement long-term solutions.
- Develop and maintain data quality controls, review processes, documentation, and operating procedures that support consistent standards.
- Create reporting frameworks and metrics to monitor data health, team performance, workload, and operational trends.
- Experiment with automation and AI-assisted workflows to improve data review processes, accuracy, and scalability.
- Evaluate and implement tools, systems, and workflows that enhance efficiency across data operations.
- Prioritize and manage data quality initiatives to ensure critical issues are addressed effectively.
- Communicate insights, trends, risks, and recommendations clearly to technical and non-technical stakeholders.
- Build, mentor, and manage a team of data quality analysts as the function grows.
- Establish best practices and maintain clear documentation to support scalable data operations.
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