Data Quality Analyst

Arrowhead Intermediaries is seeking a Data Quality Analyst to join its Data Strategy and Business Intelligence organization. The role is responsible for ensuring the quality, integrity, and reliability of data used in reporting, analytics, and machine learning applications by developing validation controls, investigating root causes, applying statistical methods, and communicating findings to stakeholders.

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