Senior Data Architect

Overview

The Senior Data Architect is a senior technical leader within Enterprise Data & Analytics (ED&A) responsible for defining, designing, and implementing enterprise-scale data architectures that enable trusted, governed, and business-ready data across the organization.

This role requires deep hands‑on expertise across transactional systems (OLTP), analytical platforms (OLAP), modern cloud data platforms, data integration, and enterprise data modeling. The Senior Data Architect is expected to operate from strategy through implementation, partnering with engineering teams to design scalable solutions while actively participating in architecture reviews, complex data modeling, performance optimization, data product design, and platform modernization initiatives.

The ideal candidate brings extensive experience designing and implementing modern data ecosystems leveraging AWS, Snowflake, Data Vault 2.0, DBT, API-based integration patterns, event-driven architectures, metadata management, and AI-ready data foundations.

• Define and evolve enterprise data architecture standards, patterns, and reference architectures.
• Develop target-state architectures supporting ED&A strategic objectives and Nexus platform evolution.
• Lead architecture design across operational, analytical, reporting, regulatory, and AI use cases.
• Drive consistency across data acquisition, storage, transformation, governance, and consumption layers.
• Establish architecture guardrails balancing agility, scalability, maintainability, and regulatory compliance.

Serve as a senior technical architect responsible for end-to-end design of the Nexus ecosystem including:

• Qlik Replicate and CDC patterns
• Event-driven ingestion architectures
• API-driven integrations
• Batch and near real-time ingestion frameworks
• External and third-party data integration

• AWS S3 data lake architecture
• Landing and ingestion zones
• Snowflake data platform architecture
• Raw Data Vault implementation
• Business Data Vault design
• Consumption and semantic layers
• Data sharing and data product architectures

• DBT architecture and modeling standards
• ELT pipeline design
• Reusable transformation frameworks
• Astronomer (Airflow) orchestration patterns
• Workflow dependency management
• Data observability and monitoring

• API-enabled data products
• Fargate-based services
• Near real-time analytics solutions
• Event streaming architectures
• Operational reporting architectures

Provide deep expertise across multiple modeling disciplines:

• Third Normal Form (3NF)
• Operational application schemas
• Transaction processing systems
• Customer, account, loan, and transaction data structures
• Source system integration patterns

• Star schemas
• Snowflake schemas
• Fact and dimension design
• Aggregate layer strategies
• Semantic modeling

• Hubs
• Links
• Satellites
• Business Vault design
• Point-in-time structures
• Bridge tables
• Auditability and lineage patterns

• Enterprise canonical models
• Business capability mapping
• Customer 360 architectures
• Reference and master data design
• Domain-driven architecture
• Architects in this role are expected to actively review and contribute to data models rather than simply approve designs.

• Drive adoption of data product thinking across ED&A.
• Define standards for ownership, accountability, quality, discoverability, and reuse.
• Partner with business domains to establish trusted and reusable analytical assets.
• Design scalable domain-oriented architectures supporting Data Mesh principles where appropriate.
• Enable self-service consumption through governed data products.

Partner closely with Governance teams to embed controls directly within architectural designs.

Responsibilities include:

• Metadata architecture
• Business glossary alignment
• Technical and business lineage
• Data quality architecture
• Data contract implementation
• Sensitive data classification
• Policy-based access control
• Data retention and auditability
• Leverage Collibra, BigID, and platform-native capabilities to improve trust and transparency across enterprise data assets.

Actively troubleshoot and improve platform performance by:

• Reviewing Snowflake query performance
• Optimizing warehouse utilization and workload management
• Designing scalable partitioning and clustering strategies
• Improving ELT processing efficiency
• Reducing data movement and duplication
• Enhancing pipeline scalability and resiliency
• Ensuring efficient storage and compute utilization
• Expected to participate in technical deep-dives and solution reviews with engineering teams.

• Define architectural foundations for enterprise AI initiatives.
• Design AI-ready data products and curated consumption layers.
• Support feature engineering and…