About the role

AI summarised

The AI Value & Business Insights Lead - Data Architect role bridges business expertise and technical data architecture to transform AI outputs into actionable insights for the Mobility Aftermarket. The incumbent designs and manages data pipelines, collaborates with AI/ML teams, and ensures data quality and governance to drive measurable business impact. Success requires deep domain knowledge in automotive/mobility aftermarket business functions, hands-on technical skills in data engineering, and the ability to communicate insights to leadership.

IndustrialOnsiteEngineering

Key Responsibilities

Understand key MA business processes (sales, pricing, trade marketing, supply chain, customer behavior)
Identify high-value AI and analytics use cases
Translate commercial questions into structured data and analytical requirements
Ensure AI initiatives directly support revenue growth, margin improvement, and efficiency
Design and manage scalable data pipelines for MA use cases
Structure, clean, and integrate data from multiple sources (ERP, Redmesh, eCommerce, etc.)
Manage and optimize cloud-based data environments
Ensure data quality, governance, and reliability for AI/analytics solutions
Collaborate with IT and central data teams to align architecture standards
Work with AI/ML teams to develop and refine use cases
Interpret model outputs and validate business relevance
Identify patterns such as price inquiries vs order conversion gaps, channel-specific margin erosion, customer churn signals

Requirements

5–10+ years experience in Automotive / Mobility Aftermarket / Trade business
Deep understanding of sales and pricing structures, channel management, logistics, finance, customer behavior, commercial KPIs
Proven track record of driving business improvements through data
Experience designing and managing data pipelines
Strong understanding of data modeling and integration concepts
Experience with cloud-based data platforms
Hands-on experience with SQL, Python
Hands-on experience with BI tools (e.g., Power BI)
Hands-on experience with workflow/automation tools (e.g., KNIME)
Understanding of data governance and quality management
Solid understanding of AI/ML concepts and model lifecycle
Experience working with AI teams to operationalize solutions
Ability to validate model logic and outputs from business perspective
Strong commercial mindset
Systems thinking — ability to connect data architecture with business outcomes
Strong communication and storytelling skills
Comfortable operating between IT, AI experts, and commercial leaders
Proactive, structured, and impact-oriented