Data Quality Analyst Job Description

Data Quality Analyst Job Description

August 5th, 2020

Data quality analysts monitor the quality of data from which organizations make informed decisions. They examine complex data to optimize the efficiency and quality of the data being collected, resolve data quality problems, and collaborate with database developers to improve systems and database designs.

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Data Quality Analyst Job Description Template

We are seeking a highly capable data quality analyst to develop procedures to enhance the accuracy and integrity of our organization's data. You will be performing data analysis, collaborating with database developers to enhance data collection and storage procedures, and preparing data analysis reports.

To ensure success as a data quality analyst, you should exhibit extensive knowledge of data analysis techniques and experience in a similar role. A top-notch data quality analyst will be someone whose data analysis expertise results in reliable information for company executives.

Data Quality Analyst Responsibilities:

  • Performing statistical tests on large datasets to determine data quality and integrity.
  • Evaluating system performance and design, as well as its effect on data quality.
  • Collaborating with database developers to improve data collection and storage processes.
  • Running data queries to identify coding issues and data exceptions, as well as cleaning data.
  • Gathering data from primary or secondary data sources to identify and interpret trends.
  • Reporting data analysis findings to management to inform business decisions and prioritize information system needs.
  • Documenting processes and maintaining data records.
  • Adhering to best practices in data analysis and collection.
  • Keeping abreast of developments and trends in data quality analysis.

Data Quality Analyst Requirements:

  • Bachelor's degree in statistics, mathematics, computer science, information management, or similar.
  • At least five years' experience in data analysis.
  • Proficiency in programming languages, including Structured Query Language (SQL) and JavaScript.
  • In-depth knowledge of statistical methods and tests.
  • Extensive experience with statistical packages, such as MS Excel, SAS, and SPSS
  • Exceptional analytical skills.
  • Advanced problem-solving skills.
  • Knowledge of best practices in data analysis.
  • Excellent interpersonal and communication skills.

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