Senior, Data Quality

SENIOR, DATA QUALITY

GRADE 35

WHQ

BECOME PART OF THE TEAM

As a Data Quality Analyst in Product Data Governance, you will primarily focus on our Data Quality program. You will be responsible for analyzing data and ensuring that it complies with our Data Quality framework. The right candidate will work with technical and non-technical stakeholders across the organization to measure the quality of our datasets across multiple data domains and help monitor compliance. 

The ideal candidate must be self-directed, comfortable supporting the needs of multiple teams, and possess strong technical acumen. The right candidate will be excited by the prospect of identifying and addressing long-term and short-term data quality remediations and being able to present the information to both technical and non-technical stakeholders.

REQUIRED SKILLS:

•Minimum of Bachelors degree in Computer Science, Data Analytics or related field or equivalent combination of education, experience or training. 

•Data Analysis: Proficient in analyzing large datasets to identify trends, patterns, inconsistencies, and potential data quality issues.

•Technical Expertise: Strong skills in SQL and Alteryx for data manipulation, transformation, and automation of data quality checks. Familiarity with Python for automation and scripting (optional).

•Data Profiling: Ability to perform data profiling to assess the quality of data sources and identify areas needing improvement.

•Data Cleansing: Expertise in data cleansing techniques to correct or remove inaccuracies, inconsistencies, and errors in data sets.

•Reporting: Capable of creating detailed reports and dashboards that highlight data quality metrics and trends, using tools like Tableau or Power BI

•Root Cause Analysis: Skilled in performing root cause analysis to determine the origins of data quality issues and propose effective solutions.

KNOWLEDGE

•Data Management Principles: Familiarity with data governance, data stewardship, and the role these play in maintaining data quality.

•Tool Proficiency: Competence in using data quality tools and platforms like Talend, Alteryx, Informatica, or similar software to automate and streamline data quality processes. Familiarity with cloud-based data platforms (Snowflake, Databricks) and their role in enterprise data quality.

•ETL Processes: Familiarity with ETL (Extract, Transform, Load) processes and how they relate to maintaining data quality during data movement and transformation.

•Data Validation Techniques: Understanding of various data validation techniques to ensure the accuracy and integrity of data.

•Business Acumen: Basic understanding of the business processes and how data quality impacts business outcomes.

BEHAVIORS

•Detail-Oriented: Pays close attention to details, ensuring that even small data issues are identified and addressed.

•Proactive: Actively seeks out potential data quality issues and works to resolve them before they become larger problems.

•Collaborative: Works effectively with team members and other departments to ensure data quality is maintained across the organization.

•Curiosity: Shows a strong desire to understand data at a deeper level, always looking for ways to improve quality.

•Problem-Solving: Demonstrates strong problem-solving skills, especially when facing complex data quality issues.

•Adaptability: Adapts quickly to new tools, technologies, and processes to improve data quality.

KEY JOB ACCOUNTABILITIES

•Data Quality Assessment and Monitoring: Perform ongoing assessments of data quality across various databases/systems and track key data quality metrics, such as accuracy, completeness, consistency, and timeliness, and report on these metrics regularly.

•Data Profiling: Analyze data sets to understand their structure, relationships, and quality, identifying any issues such as duplicates, missing values, or outliers.

•Data Cleansing and Enrichment: Develop/implement processes for cleansing data, including removing duplicates, correcting errors, and filling in missing information.

•Data Quality Standards and Procedures: Establish and maintain data quality standards and guidelines that align with organizational goals and industry best practices; create and implement procedures for data entry, validation, and maintenance to ensure high-quality data.

•Collaboration with Stakeholders: Collaborate with data stewards, IT teams, and other stakeholders to address data quality issues and implement solutions.

•Reporting and Documentation: Create and maintain reports and dashboards that provide insights into the current state of data quality; highlight trends over time.

We offer a number of accommodations to complete our interview process including screen readers, sign language interpreters, accessible and single location for in-person interviews, closed captioning, and other reasonable modifications as needed. If you discover, as you navigate our application process, that you need assistance or an accommodation due to a disability, please complete the Candidate Accommodation Request Form.