Data Quality Analyst

Built on meritocracy, our unique company culture rewards self-starters and those who are committed to doing what is best for our customers.

About the role

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.

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

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