Graph Data Engineer
Job description
About the role
You will own the end-to-end delivery of data ingestion and transformation workflows that feed mission-critical graph analytics. This role requires you to build data-agnostic foundations that allow diverse tools to operate seamlessly together. You will act as a technical liaison, translating the needs of graph data scientists into resilient platform capabilities. Your work will ensure that critical investigative platforms remain stable and responsive under strict operational timelines. You will establish durable patterns for data movement that reduce long-term maintenance overhead. Success in this position means that analysts can focus on insight rather than infrastructure friction. You will safeguard the integrity of relationships and metadata so that downstream conclusions remain valid and trustworthy.
Key facts
What you'll do
Design and implement ingestion pipelines that pull from varied structured and unstructured sources while preserving complete data lineage.
Construct graph-native representations of entities and relationships so that analysts can traverse connections with predictable performance.
Develop review and validation routines that continuously assess data quality, transformation accuracy, and adherence to operational standards.
Orchestrate the movement of refined data into the GraphAware Hume platform with an emphasis on stability, efficiency, and minimal operational disruption.
Collaborate closely with graph data scientists to align data structures and access patterns with real investigative workflows and tradecraft requirements.
Implement comprehensive monitoring of query behavior, system latency, and resource utilization to detect anomalies and guide optimization efforts.
Champion schema evolution strategies that allow the platform to adapt to new data sources without degrading long-term query reliability.
Coordinate data contracts and integration points with partner teams to ensure consistent expectations around access rules and interface behavior.
Enable analysts and decision-makers by delivering a platform that delivers dependable results even when operating under intense time pressure.
Guide cross-functional practices so that data engineering efforts remain synchronized with the evolving needs of the graph science community.
Establish operational baselines that make it easier to diagnose issues, iterate on improvements, and maintain continuity across the development lifecycle.
Support the continuous refinement of data pipelines by incorporating feedback from production environments into future design decisions.
Ensure that all technical solutions remain technology-agnostic and focused on delivering clear analytical advantage rather than platform lock-in.
Maintain a sharp focus on delivering high-quality insight through infrastructure that is both robust and intuitive for demanding users.
Requirements
Possess three to five years of experience building data platforms within demanding, high-stakes environments that require reliable outcomes.
Demonstrate a clear understanding of graph concepts, common query patterns, and storage technologies used in analytical workloads.
Show strong proficiency in Python or a comparable general-purpose language for writing transformation, integration, and automation code.
Exhibit familiarity with version control systems, testing methodologies, and deployment pipelines as part of standard engineering practice.
Display the ability to balance detailed specifications with the need for rapid delivery when priorities shift in dynamic situations.
Communicate effectively with both technical and non-technical stakeholders to clarify requirements and align on solution approaches.
Approach problem-solving with discipline, ensuring that data integrity, observability, and performance are maintained at all times.
Operate comfortably in remote settings by maintaining structured workflows and proactive communication with distributed team members.
Nice to have
Experience with GraphAware Hume or similar graph analytics platforms that emphasize relationship exploration.
Background in intelligence, law enforcement, or national security related analytical environments.
Familiarity with containerization and orchestration tools that support resilient deployment patterns.
Understanding of data governance, access controls, and compliance considerations relevant to sensitive data sets.
Practical notes
This is a remote, full-time position with compensation in the range of 170000-220000 USD. The engagement is full-time, and the role is based in a remote location with no specified travel requirements. There are no published deadlines for application submission in the provided source material.