Architecture Principal – Data & Analytics

The Architecture Principal – Data & Analytics is a Band 5 Senior Contributor responsible for defining and advancing the enterprise data and analytics architecture strategy, target states, and multi-year roadmaps across the organization.

This role serves as a recognized architecture authority, setting the northstar vision for modern data platforms, analytics, and AI-enabled capabilities,translating business priorities into pragmatic, scalable, and future-ready architectures.

Key Outcomes

• Enterprise Data & Analytics Vision: Define and publish a business-aligned data and analytics architecture strategy with a clear 3–5 year roadmap covering data platforms, governance, analytics, AI/ML, and integration layers.
• Scalable & Reusable Data Foundations: Establish modern, modular data architectures that reduce fragmentation, promote reuse, and enable enterprise-scale analytics and AI.
• Decision Velocity through Architecture Governance: Implement architecture principles, guardrails, and decision frameworks to improve speed, consistency, and quality of analytics and data platform decisions.
• AI-Ready & Insights-Driven Enterprise: Enable analytics-ready data, semantic layers, and AI capabilities that accelerate insights, automation, and intelligent decisioning.
• Cross-Domain Alignment: Act as a unifying architecture leader across Digital, Data, AI, Security, and Business domains to ensure alignment on key investment and design decisions.

Key Responsibilities

Strategy & Architecture Leadership

• Define and evolve enterprise data & analytics architecture strategy, capability maps, and target-state designs.
• Develop and maintain multi-year roadmaps aligned to business outcomes, cost optimization, and innovation priorities.
• Establish a data-as-a-product and platform-based operating model.

Data Platform & Analytics Architecture

• Architect modern data platforms (cloud data lakehouse, streaming, real-time pipelines, semantic/ontology layers).
• Define patterns for data ingestion, transformation, storage, access, and consumption.
• Enable self-service analytics, BI, and advanced analytics capabilities across the enterprise.
• Lead architecture for AI/ML platforms, feature stores, and model lifecycle integration.

Data Governance, Privacy & Compliance

• Embed data governance, quality, lineage, metadata, and stewardship frameworks into architecture design.
• Ensure architectures support regulatory, privacy, and security requirements by design.
• Establish patterns for trusted, secure, and compliant data usage.

Innovation & Emerging Capabilities

• Drive innovation in AI/ML, GenAI, agentic analytics, and intelligent automation.
• Evaluate and guide adoption of emerging technologies (e.g., knowledge graphs, semantic models, real-time decisioning).
• Lead proof-of-concepts and pilot initiatives for next-generation analytics capabilities.

Governance & Decision Enablement

• Provide architecture leadership for high-impact data and analytics initiatives.
• Establish reference architectures, reusable patterns, and standards for enterprise adoption.
• Drive alignment across portfolios to reduce duplication and optimize investments.

Delivery Partnership & Enablement

• Partner with engineering, product, and data teams to ensure architectures are executable, scalable, and cost-efficient.
• Enable adoption of modern engineering practices (DataOps, MLOps, CI/CD for data).
• Support vendor/platform strategy (e.g., lakehouse, cloud, third-party integrations).

Talent & Community Leadership

• Serve as a mentor and thought leader across the architecture and data community.
• Drive adoption of best practices, patterns, and architectural discipline.

Qualifications

Required Skills & Experience

• Bachelor’s degree in computer science, Engineering, or related field.
• 18+ years of experience in enterprise architecture, data engineering, or analytics platforms.
• Proven success defining data/analytics architecture strategies, target states, and roadmaps.
• Deep experience in large-scale, regulated enterprise environments (e.g., healthcare, financial services).
• Strong ability to influence senior stakeholders without direct authority.
• Expertise in:
• Modern data platforms (Lakehouse, Data Mesh, Data Fabric)
• Cloud ecosystems (Azure, AWS, GCP)
• APIs, event-driven architecture, and streaming
• Data governance, security, and privacy frameworks