S&P Global

posted about 2 months ago

Full-time - Mid Level
10,001+ employees
Computing Infrastructure Providers, Data Processing, Web Hosting, and Related Services

About the position

The Data Quality Engineer will play a crucial role in enhancing the accuracy and integrity of our organization's data. This position involves analyzing and monitoring the quality of data input by the organization or any automations. The Data Quality Engineer will examine complex data, collaborate with engineering groups, and develop techniques and workflows for testing. Additionally, the role includes reporting findings, setting service level standards for data quality, and ensuring that data quality issues are identified, resolved, and prevented throughout the data reliability lifecycle. Some team members will be responsible for the entirety of this lifecycle, while others will focus on resolving incidents that arise. In this role, you will be tasked with creating and implementing an enterprise-wide Data Quality (DQ) strategy by working closely with both business and technology teams to ensure alignment and dedication to objectives. You will conduct data quality analysis and discovery efforts, resolve data integrity and quality issues for major initiatives, and ensure timely and high-quality delivery of data. Your responsibilities will also include ensuring the quality, accuracy, and consistency of data by developing and implementing data quality standards, policies, and procedures. You will support efforts to cleanse and enrich data sets, including addressing duplication, standardization, and data augmentation where necessary. Furthermore, you will perform queries to identify dirty data, orphaned data, coding issues, and data exceptions. Reporting on high-level data analysis findings to management will be essential to inform business decisions and prioritize information system needs. You will also maintain data quality testing libraries to ensure ongoing effectiveness and efficiency in data quality management.

Responsibilities

  • Creating and implementing an enterprise-wide Data Quality (DQ) strategy by working with business and technology teams to ensure alignment and dedication to objectives.
  • Conduct data quality analysis and discovery efforts, resolve data integrity and data quality issues for major initiatives and ensure timely and high-quality delivery.
  • Ensure the quality, accuracy, and consistency of data by developing and implementing data quality standards, policies, and procedures.
  • Support efforts to cleanse and enrich data sets, including duplication, standardization and data augmentation where necessary.
  • Perform queries to identify dirty data, orphaned data, coding issues, and data exceptions.
  • Reporting on high level data analysis findings to management to inform business decisions and prioritize information system needs.
  • Maintain data quality testing libraries.

Requirements

  • Bachelor's degree in statistics, mathematics, computer science, information management, or similar.
  • At least 5 years of experience in data quality assurance, data analysis or a related role.
  • Proficiency in programming languages: Java and/or C#.
  • Exposure working with various database technologies (eg. MS SQL Server, PostgreSQL, MongoDB, etc.) and methodologies (Relational, NoSQL/XML/JSON, etc.).
  • Extensive experience with MS Excel, Power Query, and VBScript.
  • Exceptional analytical skills and communication skills.
  • Experience working with Tableau and Power BI for data exploration is a plus.
  • ETL tools knowledge is a plus.

Nice-to-haves

  • Experience with data visualization tools such as Tableau and Power BI.
  • Familiarity with ETL tools and processes.

Benefits

  • Health care coverage designed for the mind and body.
  • Generous time off to keep you energized.
  • Access to resources for continuous learning and career growth.
  • Competitive pay and retirement planning options.
  • Family-friendly perks and benefits for partners and children.
  • Retail discounts and referral incentive awards.
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