Sr. Data Architect

The CDP Sr. Data Architect is the technical authority for the Customer Data & Intelligence platform. This role sets and owns the architectural standards, platform patterns, and engineering quality bar that every squad builds against. The Architect is a hands-on technical leader who designs the systems, reviews the implementations, resolves the hardest engineering problems, and ensures that what gets built across squads is consistent, scalable, and built to last. This role directly shapes the quality of the customer data foundation that powers accurate, trusted profiles for every US adult available in real time, across every touchpoint.

Responsibilities:

• Own the enterprise Master Data Management (MDM) / Customer Data Platform (CDP) data architecture, defining the canonical Customer 360 data model, customer domains, and data standards across all customer data assets.
• Design and maintain conceptual, logical, and physical data models supporting customer profiles, identity graphs, interactions, consent management, householding, and customer engagement use cases.
• Define the Databricks Lakehouse architecture, including Bronze, Silver, and Gold layer design patterns, data lifecycle management, and scalable storage strategies for customer data.
• Establish identity resolution and profile unification architecture, including matching rules, survivorship logic, golden record design, and customer profile assembly frameworks.
• Design data models and architectural patterns that support near real-time (NRT) customer data ingestion, profile updates, event processing, and low-latency customer activation.
• Define enterprise standards for customer event schemas, data contracts, metadata management, lineage, and data quality to ensure trusted and reusable customer data products.
• Lead architecture reviews and guide Data Engineering teams to ensure solutions align with MDM/CDP architecture principles, scalability requirements, and best practices.
• Establish governance standards for Unity Catalogue, data security, privacy, consent management, and regulatory compliance across customer data domains.
• Collaborate with Solution Architects, Product Owners, Data Engineers, and Enterprise Architects to ensure customer data solutions are aligned with business objectives and enterprise architecture standards.
• Develop and maintain the long-term MDM/CDP architecture roadmap, enabling scalable customer data capabilities that support personalisation, advanced analytics, AI/ML, and omnichannel customer engagement.
• Drive the adoption of modern data and AI architecture practices, fostering an agentic engineering culture by defining reusable data products, semantic models, knowledge assets, and architectural patterns that enable AI agents, automation, and intelligent decision-making across the enterprise.
• Design and govern the enterprise Customer Knowledge Graph architecture, defining entity relationships, customer context models, semantic representations, and graph data standards that enable Agentic AI, intelligent customer insights, contextual reasoning, and advanced customer journey orchestration.

Requirements:

• 8 to 12 years of experience in Data Architecture, Data Engineering, or Enterprise Data Platform design, including 3+ years in a Senior Architect, Principal Engineer, or Technical Leadership role.
• Expertise in designing Master Data Management(MDM)/Customer Data Platforms (CDPs), 360 solutions, identity resolution architectures, and enterprise data models.
• Deep experience with Databricks Lakehouse architecture including Delta Lake, Unity Catalogue, medallion architecture, and scalable data platform design.
• Experience designing near real-time and event-driven data architectures using Kafka, Azure Event Hub, Structured Streaming, Delta Live Tables, or similar technologies.
• Strong expertise in conceptual, logical, and physical data modelling for large-scale enterprise customer data ecosystems.
• Experience architecting identity resolution, profile unification, customer master data, and golden record strategies using deterministic and probabilistic matching techniques.
• Experience establishing enterprise data governance, metadata management, lineage, data quality, privacy, and security standards.
• Experience designing semantic models, knowledge graphs, or AI-ready data architectures that support advanced analytics, personalisation, and Agentic AI use cases.

Nice to Have:

• Strong understanding of distributed data processing frameworks including Spark, PySpark, and cloud-native data platform architectures.
• Proven ability to influence senior technical, product, and business stakeholders and translate complex architecture decisions into measurable business outcomes.
• Experience with enterprise CDP platforms.
• Deep familiarity with identity graph design, deterministic/probabilistic match logic, and household or device-level linkage at population scale (100M+ profiles).
• Background in telco, retail, or subscription business customer data domains including event, transaction, and behavioural signal integration.
• Experience designing near-real-time profile activation pipelines that serve downstream personalisation, marketing, care, and sales use cases.
• Familiarity with privacy-by-design principles, consent management data flows, and regulatory compliance patterns (CCPA, CPRA) within a CDP architecture.
• Azure certifications or demonstrated experience with Azure-native data platform services beyond Fabric and Databricks.