Lead Analytics Solution Architect

What Do You Need to Start?

To succeed in this role, you should bring a strategic blend of analytics expertise, domain knowledge in IFAO and SCM, and strong leadership in solutioning and client engagement.

Core Skills & Experience:

• 12+ years of experience in designing and delivering enterprise analytics solutions, with a focus on IFAO (Intelligent Finance Accounts Operations)
• Proven experience in architecting analytics solutions across domains such as O2C / P2P/ R2R / Financial Planning & Analysis
• Strong understanding of IFAO processes including cost allocation, budgeting, forecasting, and performance management.
• Ability to identify and implement AI/ML use cases to drive business outcomes in finance and operations.
• Fair understanding in Analytical Tools and AI ecosystem, ETL and Visualization Tools, not restricted to:
• Power BI for enterprise-grade reporting and dashboarding.
• Azure Synapse, Azure ML, and Data Factory/
• Familiarity with ERP systems (SAP preferred, Oracle, Dynamics 365) and integration with analytics platforms.
• Strong grasp of data modelling, data integration, and governance principles.

Leadership & Communication:

• Ability to lead cross-functional teams and collaborate with finance, operations, data engineering, and delivery teams.
• Excellent communication and presentation skills to articulate solutions to technical and non-technical stakeholders.
• Experience in responding to RFPs, crafting solution proposals, and supporting go-to-market strategy.
• Ability to assess and communicate financial impact (P&L, ROI) of proposed analytics solutions.

Roles and Responsibilities

As a Lead Analytics Solution Architect (RFP), you will:

• Collaborate with product, delivery, and GTM teams to respond to RFPs with compelling and technically sound proposals.
• Lead the design and architecture of scalable, secure, and high-performance analytics solutions tailored to IFAO
• Translate business requirements into technical architecture and solution blueprints.
• Drive innovation by integrating AI/ML use cases such as financial forecasting, spend analytics, inventory optimization, and operational risk analytics.
• Ensure alignment with enterprise architecture standards, data governance, and compliance requirements.