Recruiter's Glossary

Examples: ANOVA • Reporting • Data Warehouse

Data Profiling

Term from Data Analytics industry explained for recruiters

Data Profiling is like a quality check system for company information. It's similar to how a detective examines evidence - data analysts examine datasets to understand their content, quality, and patterns. This process helps companies ensure their information is accurate, complete, and useful for making business decisions. Think of it as a health check-up for data, where analysts look for problems like missing information, incorrect entries, or inconsistent formats. This is a crucial first step before any major data analysis project, similar to how you would inspect ingredients before cooking a meal.

Examples in Resumes

Performed Data Profiling on customer databases to identify data quality issues and improve accuracy by 40%

Led Data Profiling initiatives across multiple departments to standardize data collection methods

Used Data Profiling and Data Quality Assessment techniques to clean and validate marketing datasets

Typical job title: "Data Quality Analysts"

Also try searching for:

Data Analyst Data Quality Specialist Data Quality Engineer Information Quality Analyst Data Profiling Specialist Data Quality Consultant Data Management Analyst

Where to Find Data Quality Analysts

Online Communities

Job Boards

Professional Networks

Example Interview Questions

Senior Level Questions

Q: How would you implement a data profiling strategy for a large company with multiple data sources?

Expected Answer: Should discuss a systematic approach including initial assessment, establishing data quality metrics, coordinating with different departments, creating standardized procedures, and implementing automated monitoring tools. Should mention experience leading such initiatives.

Q: How do you handle conflicting data quality requirements from different departments?

Expected Answer: Should demonstrate ability to balance different business needs, prioritize requirements, create compromise solutions, and explain complex data issues to non-technical stakeholders.

Mid Level Questions

Q: What methods do you use to identify data quality issues?

Expected Answer: Should explain practical approaches like checking for duplicate records, validating data formats, identifying missing values, and using basic statistical methods to spot unusual patterns.

Q: How do you document and report data quality findings?

Expected Answer: Should describe experience creating clear reports for different audiences, using visualizations to explain issues, and making practical recommendations for improvements.