Data Analyst

KERRIDGE COMMERCIAL SYSTEMS CORP•Whitpain Township, PA

14d•Hybrid

About The Position

Klipboard provides specialist software, services and support to deliver fully integrated trading and business management solutions to companies in the distributive trade – wherever they are in the world. With a unique depth of knowledge and experience in ERP/SaaS solutions, Klipboard has a wide range of clients includes wholesalers, distributors, merchants and retailers from small traders to multinational enterprises. Klipboard has offices in the UK, Ireland, The Netherlands, South Africa, Kenya and North America. Our mission is simple: to design and deliver high performance, integrated ERP solutions that enable our distributive trade customers to source effectively, stock efficiently, sell profitably and service competitively. Klipboard is a global, growing business that embraces AI and emerging technologies to enhance customer outcomes, collaboration, and continuous improvement. We’re looking for people who are curious about or fluid with AI, open to change, and excited to learn how technology can improve the way we work and help our customers which is always supported by strong human insight and communication. The QA Data Analyst maintains and assures the quality of Klipboard’s automotive catalog data, making sure parts, vehicle, labour and interchange information is accurate, complete and ready for customers to use. Working as a fully competent individual contributor, the role loads, reviews and validates catalog data, extracts and transforms information from industry-standard formats such as ACES and PIES, and resolves data-quality issues with limited supervision. Success looks like catalog content customers can trust – high coverage, few defects, and timely updates that reflect changes across the automotive aftermarket. Day to day, the role works within a catalog data operations team using Excel, relational databases and catalog configuration systems, and partners with product, development and customer-facing colleagues to keep data standards high. As a mid-level (P3) role, it balances hands-on data-quality delivery with growing automotive-industry and technical expertise.

Requirements

Solid data literacy – understanding of data concepts, terminology and structures.
Proficiency in Excel and data-analysis tools to review and validate large data sets.
Intermediate relational-database knowledge and data-transformation skills (for example SQL and scripting for data conversion).
Experience extracting and validating structured data such as XML feeds (ACES/PIES or equivalent).
Strong analytical skills with 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, including explaining complex data to non-technical colleagues.
Able to work as a fully competent individual contributor with limited supervision.

Nice To Haves

Familiarity with automotive catalog standards (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.
AI Fluency – Klipboard is embracing AI at pace across our products and ways of working. We’re looking for people who are curious about how AI can enhance productivity, decision-making and customer outcomes, and who are open to learning and adapting as this space evolves.

Responsibilities

Load, review and prioritise automotive catalog data updates so parts, vehicle and interchange information stays accurate and complete.
Extract and validate data from ACES and PIES XML feeds.
Verify labour and specification data, component diagrams and part interchange.
Maintain part types and product categories within 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 – installation, basic setup, part search 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.
Apply strong data literacy – data concepts, terminology and structures – to catalog data.
Analyse data trends and patterns to identify and resolve quality issues.
Demonstrate intermediate database knowledge and data-transformation capability.
Apply sound automotive parts knowledge and a developing understanding of industry trends and market dynamics.
Understand tire-data fundamentals, terminology and processes, using intermediate tools to acquire and amend tire product data.
Ensure catalog data is accurate, complete and fit for customer use.
Investigate, prioritise and remediate data-quality issues with limited supervision.
Conduct quality assurance across both the catalog system and the underlying data.
Uphold data-quality standards and governance across all deliverables.
Maintain rigorous attention to detail in all data entry and analysis.
Respond to routine customer data queries accurately and promptly.
Ensure catalog content meets customer needs and reduces coverage gaps.
Contribute to customer satisfaction through reliable, trustworthy catalog data.
Collaborate with product, development and customer-facing teams to align catalog data with requirements.
Communicate complex data clearly to non-technical colleagues.
Share knowledge and support more junior analysts.
Contribute to a culture of accuracy, accountability and continuous improvement.
Understand the roles of manufacturers, suppliers, distributors and retailers across the aftermarket.
Stay informed of industry changes – 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.