Senior Data Architect

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., Enterprise Data Architecture & Design

• 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.

Modern Data Platform Architecture

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

• 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

Data Storage & Processing Layers

• 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

Transformation & Orchestration

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

Operational Analytics

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

Enterprise Data Modeling Leadership

Provide deep expertise across multiple modeling disciplines: OLTP Modeling

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

Analytical Modeling (OLAP)

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

Data Vault 2.0

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

Information Architecture

• 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.

Data Products & Domain Architecture

• 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.

Data Governance by Design

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.

Performance Optimization & Engineering Excellence

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.

AI & Advanced Analytics Architecture

• Define architectural foundations for enterprise AI initiatives.
• Design AI-ready data products and curated consumption layers.
• Support feature engineering and model-serving architectures.
• Enable trusted, explainable, and governed AI data pipelines.
• Partner with Data Science teams to ensure scalability of predictive and generative AI solutions.
• Architect retrieval, metadata, and knowledge-layer capabilities supporting future GenAI initiatives.

Technical Leadership

• Lead architecture reviews across strategic initiatives and programs.
• Influence technical direction across engineering, analytics, governance, and business teams.
• Mentor engineers, architects, and modelers on modern architecture principles.
• Establish engineering standards, design patterns, and reusable frameworks.
• Provide thought leadership on emerging technologies and industry best practices.
• Serve as the escalation point for complex architecture and modeling challenges., Modern Data Platforms
• Snowflake
• AWS Data Services
• Amazon S3
• Data Lake and Lakehouse architectures
• Cloud-native data ecosystems

Data Engineering

• ELT/ETL architectures
• Data integration patterns
• Change Data Capture (CDC)
• Event-driven architectures
• API-based integration
• Data orchestration frameworks

Nexus Core Technologies

• Snowflake
• DBT
• Qlik Replicate
• Astronomer / Apache Airflow
• AWS
• Data Vault 2.0
• Collibra
• BigID

Data Modeling

• Conceptual, Logical and Physical Data Modeling
• Data Vault 2.0
• Dimensional Modeling
• Canonical Modeling
• Master Data Management
• Reference Data Management

Data Management

• Data Governance
• Data Quality
• Data Lineage
• Metadata Management
• Data Cataloging
• Data Contracts

Benefits are an integral part of total rewards and First Citizens Bank is committed to providing a competitive, thoughtfully designed and quality benefits program to meet the needs of our associates. More information can be found at https://jobs.firstcitizens.com/benefits. First Citizens Bank is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race (including traits historically associated with race, such as hair texture and protective hairstyles), color, religion, national origin, sex, age, disability, protected veteran status, sexual orientation, gender identity, genetic information, military membership, application, or obligation, or any other legally protected status.

Bachelor’s Degree and 6 years of experience in Enterprise data architecture, advanced data solutions, cloud platforms OR High School Diploma or GED and 10 years of experience in Enterprise data architecture, advanced data solutions, cloud platforms, * Financial Services or Banking industry experience.

• Experience modernizing legacy data warehouse environments.
• Experience architecting large-scale Customer 360 solutions.
• Experience supporting regulatory and risk reporting ecosystems.
• Knowledge of Data Mesh and Data Product operating models.
• TOGAF, CDMP, SnowPro, AWS, or equivalent certifications.

Experience

• 12+ years of experience in Data Engineering, Data Architecture, Information Management, or Analytics Engineering.
• 5+ years serving as a lead architect for enterprise-scale data platforms.
• Proven experience designing and implementing modern cloud data platforms.
• Demonstrated experience leading large-scale data modernization programs.
• Experience working directly with engineering teams on implementation, optimization, and troubleshooting.