Responsibilities
- Evaluate the current technology stack and identify opportunities to simplify, consolidate, or replace systems.
- Guide the transition to a modern, AI-enabled architecture to reduce tool sprawl and operational complexity.
- Define target architecture principles and drive migration from legacy patterns to scalable platforms.
- Assess SaaS solutions for fit and their ability to integrate into a broader, data-driven ecosystem.
- Define and lead the implementation of a scalable data platform or data warehouse, such as Microsoft Fabric or equivalent.
- Ensure data from SaaS and internal systems is centralized, structured, and accessible.
- Establish data models, governance, and ownership to support both analytics and operational use cases.
- Enable a single source of truth for reporting, decision-making, and AI applications.
- Collaborate with teams to ensure data is reliable, well-understood, and usable across the organization.
- Identify and implement AI use cases that fundamentally improve how teams work, not just incremental enhancements.
- Design architectures that embed AI into workflows, including Copilot, agents, and automation, making it part of the operating model.
- Leverage the data platform to power AI models, copilots, and decision systems.
- Define patterns for AI orchestration, data access, and agent interoperability.
- Partner with teams to rethink processes through the lens of AI-first design.
- Design end-to-end system architectures across SaaS, internal platforms, and data layers.
- Define scalable integration patterns, including APIs, event-driven, and orchestration layers.
- Ensure interoperability and reduce silos across business systems.
- Enable real-time and batch data flows into the data platform.
- Engage with stakeholders across all functions to understand how work actually happens.
- Translate business challenges into scalable, reusable system designs.
- Challenge existing processes and assumptions to unlock better ways of operating.
- Act as a trusted advisor to both technical and non-technical stakeholders.
- Establish lightweight governance to ensure security, scalability, and maintainability.
- Define standards across SaaS, data, and AI usage.
- Balance speed of innovation with long-term sustainability.
