The Taxonomy & Data Quality Analyst supports the development, maintenance, and continuous improvement of digital product taxonomy and data quality standards to ensure accurate, consistent, and reliable information across online platforms. This role works closely with cross-functional teams to analyze product data structures, maintain data integrity, and enhance digital experiences through well-organized, high-quality product content.

Qualifications Required

· Bachelor’s degree in Information Science, Data Analytics, Business, Library Science, or related field.

· Strong analytical and problem-solving skills.

· Experience working with structured data, taxonomies, metadata, or content management systems.

· Proficiency with SQL; familiarity with Python or similar tools for data manipulation.

· Ability to manage details while balancing throughput, accuracy, and deadlines.

· Strong communication skills and comfort working across functions.

· Experience with product information management (PIM) or digital content systems (Preferred).

· Exposure to data governance, data quality frameworks, or taxonomy management (Preferred).

· Background in retail, distribution, or eCommerce environments (Preferred).

· Analytical thinking.

· Data accuracy and attention to detail.

· Collaboration and teamwork.

· Curiosity and continuous improvement mindset.

· Technical aptitude for tools, data structures, and automation.

Responsibilities and Duties:

Ø Taxonomy Structure & Expansion Support – 15%

· Assist in maintaining and updating product taxonomy, category structures, and attribute schemas.

· Review classification logic to identify gaps, inconsistencies, or optimization opportunities.

· Collect input from internal partners and suppliers to support changes or enhancements to taxonomy structures.

Ø Data Governance Execution – 15%

· Support implementation of taxonomy and attribute standards, normalization rules, and data governance policies.

· Maintain documentation including data standards, metadata definitions, workflows, and process guides.

· Perform recurring data integrity checks and system audits to identify discrepancies and escalate findings.

Ø Data Quality Monitoring & Issue Resolution – 20%

· Track and report on data quality metrics to identify trends and opportunities for improvement.

· Execute data cleansing, validation, and exception-handling tasks.

· Partner with senior team members to investigate and resolve data quality issues.

Ø Technical Data Analysis & Automation Support – 10%

· Run SQL queries for audits, validation, and operational reporting.

· Use Python or SQL-based tools to automate repetitive data tasks or perform data transformations.

· Support QA workflows related to taxonomy updates, attribute changes, and content revisions.

Ø Cross-Functional Collaboration – 20%

· Work with product content, engineering, supplier data, and business teams to improve data completeness and accuracy.

· Participate in meetings to share findings and gather requirements.

· Evaluate data gaps and assist in aligning SKU build processes with documented standards.

Ø General Analyst Duties – 20%

· Support continuous improvement of work processes to increase efficiency and reduce errors.

· Develop dashboards, scripts, or monitoring tools to track compliance with data standards.

· Prepare reports and presentations for leadership as required.