Principal Data Engineer – Graph Data Engineering Neo4j

Role Summary

As Principal Data Engineer, you will be chartered with developing functional systems to realize key business objectives and goals, with a specialization in graph data engineering and connected data architecture. You will help lead a team of data engineers as you create interfaces, graph models, and data platforms that facilitate the flow, linkage, and contextualization of information across Citizens’ business operations.

In this role, you will establish and scale graph-based solutions using Neo4j, enabling relationship-driven insights, network analytics, and advanced data discovery across domains such as fraud detection, risk analysis, and customer intelligence.

Specialized Responsibilities

Serve as a key contributor in designing and delivering graph data solutions, partnering with stakeholders to translate business needs into connected data models and graph architectures
Engineer and maintain graph database Neo4j, alongside relational and non-relational systems to support hybrid data environments
Develop and operationalize relationship-based data models, including nodes, edges, and properties aligned to enterprise business domains
Design and implement knowledge graphs and connected data platforms that unify disparate data sources and expose relationships across systems
Build and optimize graph ingestion pipelines for batch and streaming data sources, ensuring data freshness and integrity
Develop mechanisms and architectures that support business line specific use cases
Establish standards and best practices for graph modeling, schema evolution, and governance within the enterprise data ecosystem
Review and manage interfaces supporting graph data access including APIs, visualization tools, and analytics platforms
Partner with data science and analytics teams to enable graph-based feature engineering and machine learning integration

Preferred Technical Expertise

Deep expertise in Neo4j platform capabilities, including clustering, security, and enterprise deployment patterns
Experience in graph data modeling and ontology design for complex enterprise datasets
Knowledge of connected data architecture patterns, including knowledge graphs and data fabrics
Experience integrating graph platforms with big data ecosystems (Spark, Kafka, etc.) and cloud-native services
Strong understanding of query optimization, indexing, and graph performance tuning
Experience with data ingestion frameworks supporting both batch and real-time pipelines
Proficiency in Python

Business Outcomes and Impacts

Enable enhanced fraud detection and prevention through network-based analysis of entities, transactions, and behaviors
Accelerate Customer 360 insights by linking fragmented data across business domains
Support real-time decisioning through connected data models and optimized graph queries
Drive improved data integrity and lineage visibility through network-based representations
Enable faster, more scalable delivery of insight-driven business capabilities through reusable graph models

Preferred Qualifications

8+ years of experience in data engineering, including experience leading engineers and technical teams
Proven experience implementing Neo4j in enterprise environments
Familiarity with machine learning and AI techniques leveraging graph data
Experience working in Agile environments and leading cross-functional delivery teams
Experience with visualization and BI tools in conjunction with graph-derived insights

Modernization and Architecture Expectations

Advance the organization’s data architecture toward connected, relationship-driven models, complementing existing data platforms
Establish graph-first design patterns where relationship complexity drives business value
Integrate Neo4j into the broader enterprise data ecosystem (cloud, lakehouse, streaming platforms)
Promote adoption of knowledge graphs and semantic modeling to improve interoperability and reuse
Implement scalable, resilient graph data platforms aligned to enterprise security and compliance standards
Standardized graph engineering practices, including modeling guidelines, performance tuning, and operational monitoring
Partner with architecture leadership to define the future-state connected data vision, ensuring alignment with digital and AI strategies

Timing