Prin AI & Analytics Data Architect

Responsibilities

• Implement and maintain a Data Catalog.
• Standardize metadata tagging and data lineage to ensure AI teams clearly understand data sources.
• Define a Semantic Layer (or metrics store) so key metrics are calculated consistently across AI models and dashboards.
• Ensure data is optimized for AI consumption, including vectorization readiness, handling unstructured content (e.g., PDFs, images), and developing “gold” standard datasets.
• Enterprise AI & Analytics Architecture (Primary)
• Define and own the enterprise AI + analytics reference architecture.
• Architect data platforms that support: Executive analytics and BI, Machine learning and generative AI, AI agents and copilots.
• Establish standards across: Data lakes, warehouses, and lakehouse patterns, Semantic layers and governed metrics, Feature stores and vector data stores.
• Ensure the architecture scales for global, multi-business use cases.
• Data Governance, Trust & Risk (By Design)
• Architect governance-first data and AI patterns, not bolt-ons.
• Ensure analytics and AI workloads comply with: Data classification and retention policies, Privacy, residency, and regulatory requirements.
• Integrate and operationalize: Data cataloging and lineage, DSPM and sensitive data monitoring.
• Platform & Data Product Leadership
• Shift the organization from: “Data pipelines” → Data products and governed assets.
• Define reusable patterns for: Ingest (ERP, CRM, SaaS, logs), Transform (ELT/semantic modeling), Serve (BI, APIs, AI, Copilots).
• Enable safe, governed access to data for: Analysts, Data scientists, AI agents and copilots, Executive & Architecture Leadership.
• Present architecture, risks, and tradeoffs to: CIO, Executive Leadership Team.
• Mentor senior architects, analytics leaders, and engineers.

Qualifications

• 12–15+ years in data architecture, analytics leadership, or platform design.
• Proven experience in designing and scaling: Enterprise analytics platforms, AI-ready data foundations.
• Strong command of: Cloud data ecosystems (AWS preferred), Analytics architectures (BI, semantic layers, metrics), AI-specific data patterns (RAG, feature stores, vector data).
• Deep understanding of governance, privacy, and security-by-design.
• Experience operating in complex, regulated, or security-sensitive environments.
• Excellent analytical and problem-solving skills.
• Excellent communication and interpersonal abilities.
• Ability to work independently and collaboratively in a team environment.

Preferred Qualifications

• Prior ownership of an Analytics or Data function.
• Expertise bridging: IT architecture, Analytics delivery, Business decision-making.
• Background working directly with executive leadership.
• Familiarity with cloud platforms (e.g., AWS, Azure).
• Knowledge of data security and privacy legislation.