Data Quality Analyst

Brown & Brown Insurance

6d•$80,000 - $95,000

About The Position

Arrowhead Intermediaries is looking for a Data Quality Analyst to join our Data Strategy and Business Intelligence organization. This role sits at the intersection of statistical rigor and data governance: you'll own the quality, integrity, and reliability of the data that powers our reporting, analytics, and downstream machine learning applications. You'll work closely with the Director of Data Products to define what “good data” means for our systems, then build the validation logic, controls, and investigative processes to enforce it. This is a great fit for someone who thinks like an analyst but cares as much about why the numbers are wrong as what the numbers say, someone comfortable owning a data quality question end to end: framing it, tracing it to root cause, and communicating the fix to both technical and non-technical stakeholders. Difficult data quality problems rarely have an off-the-shelf fix. We need someone who can reason from first principles: questioning assumptions about how the data was generated, tracing a discrepancy back to its source system, and building a systematic validation approach.

Requirements

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 an understanding of how model output is evaluated 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, comfortable tracing data discrepancy from symptom to source
A systems mindset: able to see how data flows across upstream sources, pipelines, and downstream consumers, and to spot where a fix in one place will surface problems in another
A first-principles approach to problem-solving: able to break an unfamiliar or ambiguous data quality issue down to its underlying causes rather than relying on standard playbooks
Comfortable working closely with engineering teams, whether that means partnering on pipeline changes, scoping a fix at its source, or translating a data quality issue into something an engineer can act on
Visualization and communication skills (Matplotlib, ggplot, and/or Power BI) to make findings accessible to non-technical audiences
Experience with AI tooling and LLM-based workflows (e.g., Claude, GPT) is important
A background in data science, analytics, or a related field, formal training or eq

Responsibilities

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