Senior Data Engineer

Description:

This is a greenfield, high-ownership role focused on building a data platform from the ground up. The primary focus of this position is graph data engineering and graph database architecture. You will design and implement graph-based solutions using Neo4j or another enterprise-grade graph database technology to model complex relationships, dependencies, entities, and networks.

You will architect the ingestion layer, graph and relational storage, entity resolution pipelines, data models, and APIs that unify data across customers, systems, and cloud environments. The platform will power dependency analysis, intelligent analytics, predictive capabilities, AI/ML workflows, and next-generation PP products.

Hands-on professional experience with Neo4j or another relevant graph database is mandatory. Candidates without practical graph database experience will not be considered.

Key Responsibilities
Architect and build PP's next-generation data platform with graph database technology as a core architectural component.
Design and implement graph solutions using Neo4j or comparable technologies such as Amazon Neptune, JanusGraph, TigerGraph, or similar.
Design graph data models representing operational dependencies, organizational relationships, entities, systems, and complex networks.
Develop and optimize graph traversal queries using Cypher, Gremlin, SPARQL, or comparable graph query technologies.
Build scalable ETL/ELT pipelines that ingest data from customer environments, internal systems, and third-party platforms into graph and relational stores.
Build entity resolution pipelines to match, merge, deduplicate, and link records across disparate data sources.
Design temporal and bitemporal data models to support historical analysis, replay, auditing, and versioning.
Integrate graph data with relational databases, data lakes, warehouses, and downstream AI/ML systems.
Build backend services and APIs exposing graph queries, entity resolution, and data capabilities to applications and ML systems.
Establish standards for graph data governance, quality, lineage, observability, security, and performance.
Support containerized deployments across cloud and customer-hosted environments.
Partner with product and engineering leadership to define the long-term graph and data platform roadmap.
What You'll BringRequired Skills & Experience
Mandatory: Hands-on production experience with graph databases, preferably Neo4j.
Strong experience designing and implementing graph data models for complex relationships and network-oriented data.
Strong knowledge of Neo4j and Cypher or equivalent graph database and query technologies.
Understanding of graph traversal, multi-hop relationships, relationship properties, indexing, performance optimization, and graph query patterns.
Experience determining when a graph-based architecture is more appropriate than a traditional relational model.
Strong SQL expertise and experience designing performant relational data models.
Experience with entity resolution, record linkage, deduplication, or data matching at scale.
Experience building data lakes, warehouses, and distributed data platforms.
Strong understanding of ETL/ELT patterns, orchestration, and pipeline reliability.
Experience with Airflow, Dagster, dbt, or comparable orchestration/transformation technologies.
Experience designing enterprise integrations, connectors, APIs, and backend data services.
Strong Python or Java development skills.
Experience with cloud-native data platforms; Azure experience is preferred.
Strong understanding of scalability, performance, monitoring, security, and data quality.
Experience with Docker, Kubernetes, or similar containerized infrastructure is a plus.
Experience with temporal or bitemporal data modeling is a plus.
Experience with Salesforce or ServiceNow data models and integrations is a plus.
Familiarity with AI-assisted development tools such as GitHub Copilot, Cursor, or Claude Code.
Product-oriented mindset with the ability to make pragmatic architectural decisions in an early-stage environment.
Graph Database Expertise — Mandatory
Graph database experience is the most important qualification for this position.

Candidates must have hands-on experience with at least one relevant graph database or graph data platform, including:

Neo4j — strongly preferred
Amazon Neptune
JanusGraph
TigerGraph
ArangoDB
Azure Cosmos DB Gremlin API
Other comparable enterprise graph database technologies
Candidates should be able to demonstrate experience with:

Graph data modeling
Nodes, relationships, and properties
Cypher, Gremlin, SPARQL, or equivalent query languages
Multi-hop graph traversal
Graph indexing and query optimization
Large-scale relationship modeling
Graph-to-relational integration
Graph-based entity resolution
Production graph database deployment and operations
This is not a traditional SQL-only Data Engineering position. Strong graph database experience is a mandatory requirement.

Qualifications
Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field.
5+ years of experience in data engineering, backend data systems, platform engineering, or related roles.
Professional experience building and operating graph database solutions is mandatory.
Experience building or significantly expanding production-grade data platforms.
Experience working with highly relational, graph, or network-oriented data structures.
Experience designing enterprise-grade integrations and connectors.
Experience with cloud-native environments, preferably Azure.
Experience with entity resolution or record matching is a plus.
Experience with Docker and Kubernetes is a plus.