Data Architect-1994

The AI Data Enablement Architect is responsible for enabling PIP’s enterprise data to be AI-ready, agent-consumable, and decision-aware, with a strong focus on Azure-native services and Foundry as core components of the data ecosystem. This role bridges enterprise data architecture, data engineering, and AI enablement by ensuring data is semantically rich, governed, observable, and operationalized for use by Agentic AI, copilots, and intelligent automation. The Architect partners closely with Data Engineering, Enterprise Architecture, AI/ML teams, and business stakeholders to establish standards and patterns that allow AI systems to safely reason over PIP data and support autonomous or semi-autonomous decision-making. This role plays a key part in modernizing PIP’s global data warehouse and operational data platforms to support AI-driven insights, process automation, and scalable decision intelligence.

AI-Ready Data Architecture (Azure & Foundry)

• Design and evolve data architecture patterns that enable AI agents and copilots across PIP’s Azure data platform and Palantir Foundry environment.
• Partner with Data Engineering to ensure data pipelines, ontologies, and object models in Foundry are optimized for AI consumption, not just analytics.
• Define standards for structuring operational, transactional, and analytical data to support: o Retrieval-Augmented Generation (RAG) o Agent tool usage o Stateful and event-driven decision workflows.
• Ensure alignment between Foundry, Azure Data Lake, and the Global EDW to provide consistent and trusted data access.

Semantic Layer & Business Context Enablement

• Own and evolve enterprise semantic models that represent PIP business concepts (customers, products, pricing, supply chain, financials, etc.).
• Leverage Foundry ontologies and semantic layers to ensure AI systems correctly interpret business meaning, metrics, and relationships.
• Partner with business leaders and data product owners to translate business decisions and workflows into machine-understandable data constructs.
• Ensure consistency of definitions across BI, Foundry applications, AI use cases, and downstream consumers.

Metadata, Lineage & Data Contracts.

• Define and operationalize data contracts for AI consumption, including schema, freshness, quality thresholds, lineage, and usage constraints.
• Ensure metadata and lineage captured in Foundry and Azure are accessible and actionable by AI systems and governance processes.
• Establish standards for dataset certification, confidence scoring, and trust indicators to support autonomous data usage.

Data Quality, Observability & Trust

• Define AI-specific data quality dimensions such as: o Semantic accuracy o Data drift o Context completeness o Impact on AI-driven decisions
• Partner with Data Engineering to implement observability and monitoring across Azure and Foundry pipelines.
• Proactively identify and remediate data issues that could degrade AI outputs, automation accuracy, or business trust. --- Governance, Security & Responsible AI
• Extend PIP’s existing data governance and MDM frameworks to support AI and agentic use cases.
• Define guardrails for AI data access, including: o Role-based access controls o Sensitive data handling o Human-in-the-loop requirements
• Partner with Security, Legal, Compliance, and Risk teams to ensure AI data usage aligns with SOX, GDPR, CCPA, and internal control standards.
• Support auditability and explainability of AI-driven outcomes through strong data lineage and governance.

Cross-Functional Collaboration & Enablement

• Serve as a central point of expertise for AI data readiness across Data & Analytics, IT, and business teams.
• Collaborate with: o Data Engineers building Azure and Foundry pipelines o AI/ML teams developing copilots and agents o Business stakeholders driving AI-enabled use cases
• Define reference architectures, standards, and playbooks for onboarding new AI use cases and agents within PIP.

Continuous Improvement & Platform Evolution

• Contribute to the roadmap for AI data enablement across Azure, Foundry, and the Global EDW.
• Evaluate emerging capabilities within Azure and Foundry that improve AI readiness, semantic modeling, and governance.
• Support data migration, enrichment, and transformation initiatives tied to system integrations, acquisitions, and platform modernization