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
