Verified Job
Engineering - Architecture
First Citizens Bank Senior Data Architect
Engineering - Architecture
#802814
Remote / WFH
First Citizens Bank
Job Description
Job highlights
Identified by Google from the original job post
Qualifications
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
Business capability mapping
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
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
Modern Data Platforms
Snowflake
Amazon S3
Data Lake and Lakehouse architectures
Cloud-native data ecosystems
Data Engineering
ELT/ETL architectures
Data Modeling
Conceptual, Logical and Physical Data Modeling
Master Data Management
Data Governance
Data Lineage
Benefits
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
Responsibilities
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
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
API-enabled data products
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
Fact and dimension design
Aggregate layer strategies
Semantic modeling
Satellites
Auditability and lineage patterns
Enterprise canonical models
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
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
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
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
Data integration patterns
Change Data Capture (CDC)
Event-driven architectures
API-based integration
Data orchestration frameworks
Reference Data Management
Metadata Management
Data Cataloging
Job description
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.
Responsibilities
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.
Qualifications
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
Preferred Qualifications
• 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.
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.
Identified by Google from the original job post
Qualifications
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
Business capability mapping
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
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
Modern Data Platforms
Snowflake
Amazon S3
Data Lake and Lakehouse architectures
Cloud-native data ecosystems
Data Engineering
ELT/ETL architectures
Data Modeling
Conceptual, Logical and Physical Data Modeling
Master Data Management
Data Governance
Data Lineage
Benefits
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
Responsibilities
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
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
API-enabled data products
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
Fact and dimension design
Aggregate layer strategies
Semantic modeling
Satellites
Auditability and lineage patterns
Enterprise canonical models
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
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
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
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
Data integration patterns
Change Data Capture (CDC)
Event-driven architectures
API-based integration
Data orchestration frameworks
Reference Data Management
Metadata Management
Data Cataloging
Job description
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.
Responsibilities
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.
Qualifications
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
Preferred Qualifications
• 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.
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.
