Responsibilities & Key Deliverables
The Analytics & Digital Transformation Manager will drive data-led decision-making within the Spares Business Unit (SBU) by translating business requirements related to automation, process optimization, revenue growth, and customer experience into effective digital solutions and implementation plans.
A. System Development & Integrations
Develop enterprise dashboards using Power BI, Qlik, and cloud analytics platforms.
Build self-service reporting solutions to improve visibility and decision-making.
Support integration of SAP, ERP, Dealer Management Systems, CRM platforms, Data Lakes, and external data sources.
Design scalable reporting frameworks, KPI governance models, and real-time Control Tower dashboards.
Ensure data accuracy, consistency, and standardization across reporting platforms.
Coordinate with IT and Data Engineering teams for automated data integration and deployment.
B. Data Analytics
Enable data-driven decision-making through analytics, dashboards, and digital applications.
Conduct root cause analysis and recommend performance improvements.
Develop predictive and prescriptive analytics use cases, including Demand Forecasting, Inventory Optimization, Fill Rate Prediction, Backorder Risk Prediction, and Channel Performance Analytics.
Leverage AI, Machine Learning, and GenAI technologies to enhance business outcomes.
C. Business & Stakeholder Engagement
Partner with business leaders and cross-functional teams to understand priorities and requirements.
Lead workshops, requirement-gathering sessions, and process reviews.
Serve as the single point of contact for analytics and digital transformation initiatives within the SBU.
Translate business challenges into data, process, and technology solutions.
Develop business cases, ROI assessments, and implementation roadmaps while managing relationships with stakeholders, consultants, and technology partners.
D. Process Improvement & Automation
Identify and implement automation and process optimization opportunities.
Drive automation of MIS, reporting, and business workflows using digital and low-code/no-code solutions.
Eliminate manual and non-value-added activities through continuous improvement initiatives.
Track and measure benefits realized from automation programs.
E. System & Process Efficiency
Analyze processes across Sales, Supply Chain, Inventory, Logistics, and Aftermarket functions.
Identify bottlenecks, improve productivity, service levels, and customer experience through process re-engineering.
Establish KPI frameworks, standardize reporting methodologies, and enhance decision-making through real-time visibility and governance.
F. Digital Architecture & Governance
Define and maintain the long-term analytics, data, and digital architecture roadmap.
Design scalable data architecture supporting analytics, automation, and AI initiatives.
Support Data Lakes, Data Warehouses, Cloud Analytics Platforms, SAP/ERP integrations, and enterprise reporting systems.
Establish data governance, master data management, and a single source of truth across reporting systems.
Collaborate with IT to build secure, scalable, and future-ready digital ecosystems that support business growth.
Preferred Industries
Auto / Auto Equipment
Auto Components
Education Qualification
The ideal candidate will possess a strong educational foundation that combines technical expertise with business acumen to effectively drive analytics and digital transformation initiatives.
Master of Business Administration (MBA): Preferably from a reputed institution, providing a comprehensive understanding of business strategies, financial principles, and management techniques essential for aligning data initiatives with organisational objectives.
Bachelor of Engineering (B.E.): A degree in engineering disciplines such as Computer Science, Information Technology, Industrial Engineering, or related fields to ensure a robust grasp of technical processes, system integrations, and digital technologies.
Additional certifications or coursework in data analytics, business intelligence, project management (e.g., PMP), Lean Six Sigma, or relevant technology platforms (such as Power BI or SAP) would be advantageous, reflecting a commitment to continuous learning and professional development.
General Experience
Candidates should bring between five to eight years of progressive experience, ideally accumulated within industries related to automotive, auto equipment, or auto components. Experience should demonstrate a blend of technical proficiency and business insight.
Proven track record of managing and executing analytics and digital transformation projects that have delivered measurable business value.
Experience collaborating across cross-functional teams including IT, sales, supply chain, and aftermarket service departments.
Adept in the end-to-end lifecycle of data solution development—from requirement gathering, to design, integration, deployment, and ongoing optimisation.
Comfortable with stakeholder management at various organisational levels, including senior leadership engagement and vendor partnerships.
Familiarity with methodologies such as Lean, Six Sigma, and Agile project management, underscoring a disciplined approach to process improvement.
Hands-on experience with data visualisation tools, business intelligence platforms, and ERP systems.
Broad exposure to large-scale enterprise environments and the challenges therein will be highly beneficial.
Critical Experience
Functional Expertise
Exceptional analytical thinking and complex problem-solving skills that enable identification and resolution of intricate business challenges through data.
Comprehensive experience with SAP, ERP platforms, and supply chain systems to understand and improve business processes.
Advanced proficiency in business intelligence tools including Power BI and Excel Automation, complemented by strong data modelling capabilities.
Solid understanding of operations across sales, supply chain, and inventory management to create impactful analytics solutions.
Technical Competencies
Expertise in design and development of data visualizations and dashboards that simplify complex data into accessible insights.
Strong skills in data extraction, transformation, and loading techniques to manage and prepare data efficiently.
Working knowledge of BigQuery and SQL is preferred, enabling effective querying and manipulation of large datasets.
Familiarity with cloud analytics platforms and emerging technologies such as AI, Machine Learning, and Generative AI to foster innovation.
The successful candidate will continuously update their technical skill set in tune with evolving industry standards and technological advancements.
System Generated Core Skills
Analytical Thinking
Project Management
System Generated Secondary Skills
Job Segment: Industrial Engineer, Engineer, Engineering, Automotive
