Data Quality Assurance Analyst
We are looking for a detail-oriented and analytical Data Quality Assurance Analyst to help ensure the accuracy, completeness, consistency, and reliability of large datasets and data products.
In this role, you will review and validate structured data, investigate anomalies, identify inconsistencies, and document findings to support continuous data quality improvement. You will work closely with cross-functional teams to investigate data issues, determine their root causes, and establish systematic processes to prevent recurring errors.
The ideal candidate has a strong investigative mindset, enjoys working with data, and can combine careful manual investigation with data analysis and automated quality-control techniques.
Responsibilities:
Perform quality assurance testing on datasets and data products before and after production releases.
Evaluate data for accuracy, completeness, consistency, validity, uniqueness, timeliness, and referential integrity.
Identify anomalies, missing information, duplicate records, incorrect classifications, inconsistent values, and other data quality issues.
Validate business and entity information against authoritative or reliable external sources.
Conduct sample-based and systematic testing across large datasets.
Investigate reported data issues, identify root causes, and recommend appropriate corrective actions.
Develop and maintain data quality rules, validation criteria, test cases, and quality assurance procedures.
Collaborate with data engineering teams to translate identified issues into automated validation and monitoring rules.
Perform regression testing following changes to data pipelines, matching algorithms, source data, schemas, or transformation logic.
Monitor data quality metrics and identify unexpected changes or deterioration in data quality.
Evaluate entity matching, deduplication, standardization, classification, and record-linkage results.
Review company names, addresses, identifiers, industry classifications, geographic information, entity relationships, and other key attributes for logical consistency.
Document data issues, including examples, severity, potential causes, affected records, and recommended remediation.
Maintain issue logs and track identified data quality problems through resolution.
Help establish measurable data quality benchmarks and acceptance thresholds.
Work with cross-functional teams to continuously improve data quality and reliability.
Qualifications:
Bachelor's degree or equivalent professional experience in data analytics, information systems, computer science, economics, statistics, business, accounting, or a related field.
Strong attention to detail and the ability to identify subtle inconsistencies in data.
Strong analytical, investigative, and problem-solving skills.
Experience working with structured datasets, databases, spreadsheets, or data management systems.
Ability to understand relationships among businesses, legal entities, locations, people, and other real-world entities.
Ability to document findings clearly and communicate technical data issues to both technical and nontechnical colleagues.
Strong organizational skills and the ability to manage multiple quality assurance investigations simultaneously.
Ability to work independently while following established quality assurance procedures and escalation processes.
Preferred Qualifications:
SQL and relational databases
Python or another scripting language
Data profiling and validation
Data cleansing and standardization
Entity resolution and record linkage
Master Data Management (MDM)
Knowledge graphs or graph databases
Business and legal-entity data
Address standardization and geospatial data
Data pipelines and ETL/ELT processes
Automated data testing
Statistical sampling
Data quality monitoring and dashboards
Government, financial, economic, or commercial datasets
Artificial intelligence (AI), machine learning, Retrieval-Augmented Generation (RAG), or GraphRAG data preparation