Data Architect

Role Overview

The Data Architect within the Data & Analytics Center of Excellence (CoE) plays a critical enterprisewide leadership role in shaping and governing EPDs data foundations as the organization evolves toward a productcentric federated (Data Meshaligned) operating model.

This role is accountable for defining enterprise data architecture data models semantics integration patterns and governance frameworks that enable domain teams to build and operate trusted observable and AIready data products. It ensures data across core systems and functional domains (e.g. Commercial Finance Operations) is consistently defined discoverable and fit for analytics digital AI and Generative AI (GenAI) use cases.

Operating at enterprise scale the Data Architect translates business and analytics needs into robust reusable and governed data architectures accelerating time to value while ensuring longterm sustainability trust and compliance.

Key Responsibilities

Enterprise & Federated Data Architecture Leadership

• Define and govern the enterprise data architecture strategy supporting a federated domainoriented and productcentric data model.
• Establish architecture standards guardrails and interoperability patterns that enable domain autonomy while preserving enterprise coherence.
• Own and maintain reference architectures for data platforms data products integration and consumption across analytics and AI use cases.
• Ensure data architectures are scalable secure resilient and aligned with enterprise and platform roadmaps.
• Delivery of logical and physical data models that translate business processes into analytics-ready structures enabling data products and effective data consumption

Data Products Modelling & Semantics

• Lead dataasaproduct architecture defining clear ownership contracts SLAs and lifecycle standards for data products.
• Define and govern enterprise semantic consistency including canonical entities shared dimensions KPIs and business metrics.
• Translate business and analytical requirements into robust conceptual logical and physical data models.
• Promote consumercentric data design that supports analytics operational use cases and machine consumption.

Master Data Management (MDM) & Governance at Scale

• Architect MDM and reference data solutions that balance domain ownership with enterprise consistency.
• Define patterns for domainaligned golden records hierarchies and shared master data.
• Lead federated data governance covering ownership stewardship quality lineage metadata access controls and compliance.
• Ensure governance is embedded via architecture standards automation and tooling rather than centralized bottlenecks.

Data & Data Product Observability

• Define architectural standards for data and data product observability across the federated landscape.
• Ensure data products expose measurable indicators for quality freshness completeness availability lineage and usage.
• Promote monitoring alerting and operational reliability patterns that build trust in enterprise analytics and AI consumption.
• Champion observability as a core foundation for selfservice analytics and AI adoption.

Analytics AI & GenAI Enablement

• Architect data foundations that support advanced analytics machine learning and GenAI at enterprise scale.
• Design data product interfaces and models supporting feature reuse embeddings vector stores and RetrievalAugmented Generation (RAG) patterns.
• Partner with Enterprise Architect and platform teams to ensure data products are AIready by design not retrofitted.
• Define and enforce data governance quality and security standards.
• Ensure compliance with regulated pharma data requirements.
• Align with privacy security and compliance teams

Collaboration Influence & Enablement

• Act as a senior architectural advisor to domain platform and governance leaders.
• Mentor data architects and senior engineers on productcentric and Data Meshaligned best practices.
• Work closely with product owners analysts and engineers to translate business needs into data solutions
• Represent the Data & Analytics CoE in enterprise architecture forums driving alignment across domains and initiatives.
• Influence outcomes through thought leadership clarity and architectural credibility.

Required Experience & Capabilities

• Extensive experience as a Senior or Principal Data Architect in large complex enterprise environments.
• Proven delivery of enterprise data architectures aligned with Data Mesh or productcentric models.
• Deep expertise in:
  • Enterprise data modeling and semantics
  • Master Data Management (MDM)
  • Data integration and harmonization patterns
  • Federated data governance frameworks
• Strong handson experience with data modelling and data harmonizaton design.
• Strong experience designing and operating data products with clear ownership contracts and observability.
• Demonstrated success scaling governance through standards automation and architectural guardrails.
• Strong domain knowledge across enterprise functions such as Commercial Marketing Finance Operations or Supply Chain
• Solid understanding of AI and Generative AI data requirements including features embeddings and RAG patterns.
• Strong stakeholder management and seniorlevel influencing skills.

Preferred Experience & Technologies

• Handson data architectural experience with cloudnative data platforms including:
  • AWS as the cloud foundation
  • Databricks as the enterprise data platform
  • dbt for analytics engineering and transformations
• Experience with data modeling tools (e.g. Idera or equivalent).
• Familiarity with data observability tooling (e.g. Elementary or similar).
• Experience with data catalogs metadata management and lineage solutions.
• Familiarity with PMO processes SDLC / SLC governance and architecture documentation standards.
• Exposure to pharma or life sciences data environments preferred

Travel Requirements

The role includes occasional international travel to support key business activities such as stakeholder meetings workshops and team events.

Why Join the Data & Analytics CoE (EPD)

• Shape EPDs enterprise evolution toward dataasaproduct at scale.
• Influence how domains deliver trusted observable and AIready data products.
• Operate at a strategic level within a mature forwardthinking Data & Analytics CoE.
• Leave a lasting architectural impact on EPDs analytics and AI foundations.