We are seeking a detail-oriented and analytical Data Quality Analyst to join our team. In this role, you will play a crucial part in ensuring the accuracy, consistency, and completeness of our data assets. Your primary responsibilities will involve identifying and resolving data quality issues, standardizing data formats, and providing timely responses to inquiries from both internal and external stakeholders.
Key Responsibilities:
- Data Quality Assurance:
- Identify and resolve data quality issues, such as missing values, inconsistencies, and errors.
- Conduct data audits and perform root cause analysis to pinpoint the source of data quality problems.
- Develop and implement data quality standards and procedures to maintain data integrity.
- Data Cleaning and Standardization:
- Cleanse and standardize data to ensure consistency and accuracy.
- Transform data into a standardized format, resolving inconsistencies in data entry and formatting.
- Develop and maintain data dictionaries and metadata to document data definitions and usage.
- Data Analysis and Reporting:
- Utilize SQL and reporting tools to analyze data and generate insightful reports.
- Leverage advanced Excel skills to manipulate and analyze complex datasets.
- Create visualizations and dashboards to communicate data findings effectively.
- Stakeholder Communication:
- Field and respond to inquiries from third-party vendors and clients regarding data issues and requests.
- Communicate effectively with technical and non-technical stakeholders to explain complex data concepts.
- Provide timely updates on data loading processes and status.
- Degree or equivalent experience in information systems, statistics, or a related field.
- Proven experience in data quality analysis, data cleaning, and data validation.
- Strong proficiency in SQL, Excel, and reporting tools (e.g., Power BI, Tableau).
- Excellent analytical and problem-solving skills.
- Strong attention to detail and accuracy.
- Excellent written and verbal communication skills.
- Ability to work independently and as part of a team.
- Experience with data warehousing and ETL processes.
- Knowledge of data governance and data management best practices.
- Familiarity with scripting languages (e.g., Python, R).
