Role Overview
The Data Architect within the Data & Analytics Center of Excellence (CoE) plays a critical, enterprise‑wide leadership role in shaping and governing EPD’s data foundations as the organization evolves toward a product‑centric, federated (Data Mesh–aligned) 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 AI‑ready 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 long‑term sustainability, trust, and compliance.
Key Responsibilities
Enterprise & Federated Data Architecture Leadership
- Define and govern the enterprise data architecture strategy supporting a federated, domain‑oriented, and product‑centric 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 data‑as‑a‑product 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 consumer‑centric 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 domain‑aligned 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 self‑service 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 Retrieval‑Augmented Generation (RAG) patterns.
- Partner with Enterprise Architect and platform teams to ensure data products are AI‑ready 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 product‑centric and Data Mesh–aligned 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 product‑centric models.
- Deep expertise in:
- Enterprise data modeling and semantics
- Master Data Management (MDM)
- Data integration and harmonization patterns
- Federated data governance frameworks
- Strong hands‑on 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 senior‑level influencing skills.
Preferred Experience & Technologies
- Hands‑on data architectural experience with cloud‑native 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 EPD’s enterprise evolution toward data‑as‑a‑product at scale.
- Influence how domains deliver trusted, observable, and AI‑ready data products.
- Operate at a strategic level within a mature, forward‑thinking Data & Analytics CoE.
- Leave a lasting architectural impact on EPD’s analytics and AI foundations.