Data Analyst

KERRIDGE COMMERCIAL SYSTEMS CORP•Cary, NC

13d•Hybrid

About The Position

The QA Data Analyst maintains and assures the quality of Klipboard’s automotive catalog data, ensuring that parts, vehicle, labour, specification and interchange information is accurate, complete and ready for customer use. This is a mid-level individual-contributor role that works with limited supervision, loading, reviewing and validating catalog data; extracting and transforming data from formats such as ACES and PIES; and investigating and resolving data-quality issues. The role works within a catalog data operations team using Excel, data-analysis tools, relational databases and catalog configuration systems. It collaborates with product, development, engineering, customer support and other colleagues to maintain data standards, meet customer needs and deliver reliable catalog updates. The analyst also responds to routine customer data queries, shares knowledge with junior analysts, and contributes to continuous improvement. Klipboard encourages employees to use AI tools to improve data quality and productivity. Klipboard’s work policy is hybrid, with employees spending three days in the office and two days working from home.

Requirements

Solid data literacy, including understanding data concepts, terminology and structures.
Proficiency in Excel and data-analysis tools for reviewing and validating large data sets.
Intermediate relational-database knowledge and data-transformation skills, such as SQL and scripting for data conversion.
Experience extracting and validating structured data such as XML feeds (ACES/PIES or equivalent).
Strong analytical skills and a high level of accuracy and attention to detail.
Knowledge of automotive parts and their functions, or equivalent product-data domain experience.
Clear written and verbal communication skills, including the ability to explain complex data to non-technical colleagues.
Ability to work as a fully competent individual contributor with limited supervision.

Nice To Haves

Familiarity with automotive catalog standards, including ACES, PIES and Partslink.
Understanding of automotive industry trends and market dynamics.
Tire-data experience, including tire-industry terminology and data-acquisition tools.
Experience with catalog configuration systems.
Exposure to verifying component diagrams, interchange and labour/specification data.
Curiosity about how AI can enhance productivity, decision-making and customer outcomes, and openness to learning and adapting as AI evolves.

Responsibilities

Load, review and prioritise automotive catalog data updates to keep parts, vehicle and interchange information accurate and complete.
Extract and validate structured data from ACES and PIES XML feeds.
Verify labour and specification data, component diagrams and part interchange.
Maintain part types and product categories in the catalog.
Publish and recommend retained parts based on data review.
Use Excel and data-analysis tools to cleanse, review and validate large volumes of catalog data.
Perform catalog configuration tasks, including installation, basic setup, part searches and user emulation.
Develop and apply scripts and tools for data conversion using intermediate data-transformation techniques.
Query and analyse complex data structures across relational databases.
Apply ACES, PIES and Partslink catalog standards in day-to-day work.
Analyse data trends and patterns to identify and resolve quality issues.
Use intermediate tools to acquire and amend tire product data.
Investigate, prioritise and remediate data-quality issues with limited supervision.
Conduct quality assurance across the catalog system and its underlying data, upholding data-quality standards and governance.
Respond accurately and promptly to routine customer data queries, helping meet customer needs and reduce coverage gaps.
Collaborate with product, development, engineering, customer-facing and data teams to align catalog data with requirements.
Explain complex data clearly to non-technical colleagues, share knowledge and support more junior analysts.
Stay informed about aftermarket industry changes, including mergers and acquisitions, private-label supplier changes and emerging technologies.
Identify product-relevant industry and tire-data trends.
Use AI tools to improve data quality and productivity in line with Klipboard’s approach to AI.