Data Quality Analyst

Brown & Brown Insurance

. Design and maintain validation rules, reconciliation processes, and quality checks across systems of record .

Posted 9/19/2026full-timeRemote • United StatesMid-LevelSenior💰 $80,000 - $95,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Proficient in designing and maintaining data validation rules and quality controls, with strong skills in Python/R and SQL for data profiling and cleaning. Capable of applying statistical analysis and machine learning concepts to ensure data integrity and support decision-making.

Highest-signal resume keywords

Python/R Data ProfilingSQL Data ValidationStatistical AnalysisMachine Learning EvaluationData Quality Metrics

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data Validation RulesReconciliation ProcessesData CleaningData StructuringRoot-Cause InvestigationDescriptive StatisticsInferential StatisticsData Pipeline DevelopmentData Profiling WorkflowsFirst-Principles Problem-Solving

Soft Skills

CollaborationCommunication SkillsVisualization Skills

Tools & Technologies

MatplotlibGgplotPower BIAI ToolingLLM-Based Workflows

Industry Keywords

Data ScienceAnalyticsData IntegrityQuality ControlsStatistical Methods

Tech Stack

Tools & technologies

PythonSQL

About the role

Key responsibilities & impact

Design and maintain validation rules, reconciliation processes, and quality checks across systems of record
Investigate data integrity issues and trace discrepancies between platforms and systems back to root cause
Build and maintain data pipelines and profiling workflows in Python/R and SQL for cleaning, structuring, and validating incoming data
Partner with data engineering and BI teams to define review and approval controls for data entering reporting and analytics pipelines
Apply descriptive and inferential statistical methods to detect anomalies, quantify data quality issues, and support root-cause analysis
Evaluate machine learning or scoring model behavior and output against real-world outcomes, flagging drift or inconsistency
Translate data quality findings into actionable reporting for non-technical decision-makers and leadership
Collaborate with stakeholders to define data quality metrics, standards, and reporting cadences

Requirements

What you’ll need

Experience with Python/R and SQL for data profiling, cleaning, and validation
Familiarity with statistical analysis: hypothesis framing, exploratory data analysis, interpretation of uncertainty
Exposure to machine learning concepts and understanding of model output evaluation for quality and consistency
Demonstrated experience designing data validation rules, reconciliation processes, or quality controls across multiple systems of record
Strong root-cause investigation skills
Systems mindset across upstream sources, pipelines, and downstream consumers
First-principles approach to problem-solving
Comfortable working closely with engineering teams
Visualization and communication skills using Matplotlib, ggplot, and/or Power BI
Experience with AI tooling and LLM-based workflows, such as Claude and GPT
Background in data science, analytics, or a related field; formal training or equivalent experience

Benefits

Comp & perks

Health Benefits: Medical/Rx, Dental, Vision, Life Insurance, Disability Insurance
Financial Benefits: ESPP; 401k; Student Loan Assistance; Tuition Reimbursement
Mental Health & Wellness: Free Mental Health & Enhanced Advocacy Services
Paid Time Off
Holidays
Preferred Partner Discounts