About the role
AI summarisedLead end-to-end architecture for data, analytics, and AI solutions within the investment management ecosystem. This role requires strong technical leadership in designing scalable data warehouse and lakehouse architectures while effectively communicating complex concepts to diverse stakeholders.
ConsultingOnsiteAssurance
Key Responsibilities
- Design and lead end-to-end architecture for data, analytics, and AI solutions across the investment management ecosystem.
- Develop scalable, secure, and governed data warehouse and data lakehouse architectures using Databricks and Microsoft Fabric.
- Define integration patterns, data pipelines, processing frameworks, and consumption layers for enterprise-grade systems.
- Evaluate and align architectural decisions with business strategy, regulatory requirements, and technology roadmap.
- Craft compelling narratives to explain technical concepts, solution designs, and roadmaps to both technical and non-technical audiences.
- Guide the adoption and integration of modern AI technologies, including Gemini AI, into data and analytics workflows.
- Provide architectural oversight throughout the project lifecycle, including requirements, design, build, testing, and deployment.
Requirements
- Apply advanced knowledge of data warehousing, data modeling, metadata management, data governance, and data quality frameworks.
- Proven ability to influence cross-functional teams across business, data, and engineering stakeholders.
- Experience leading architecture workshops and executive briefings.
- Ability to translate complex architectures into intuitive visuals, models, and presentations.
- Ensure best practices related to security, privacy, compliance, and cloud architecture are maintained.
- Experience in solution feasibility assessments and performance optimization.