Knowledge Graph / Data Integration Engineer

Full-Time | Contract-to-Hire | Remote (U.S.)

OtterSoft is building an AI-ready workforce management platform powered by a sophisticated semantic data architecture. We are seeking a Knowledge Graph / Data Integration Engineer to build and maintain the pipelines and services that keep our ontology accurate, connected, and current.

Every shift match, compliance alert, and AI-powered recommendation depends on reliable data flowing from operational systems into the platform. In this role, you will integrate data from applicant tracking, vendor management, credentialing, scheduling, timekeeping, payroll, and other business systems, helping ensure that downstream applications and AI agents can rely on consistent, high-quality information.

You will work closely with the Ontology Architect, product, engineering, and business stakeholders to implement semantic data integrations, operationalize business logic, and build dependable services for applications, dashboards, and AI-enabled workflows.

What You'll Do

• Build and maintain data pipelines that load source-system data into ontology objects and relationships in both batch and near-real-time processing environments.
• Implement entity resolution and identity-matching processes so each person, facility, and unit can be represented consistently across connected systems.
• Implement derived properties and business rules defined by the Ontology Architect, including shift eligibility, credential-expiration windows, and margin per assignment.
• Build data-quality checks, monitoring, and alerting; investigate and resolve pipeline failures and data issues before they affect recruiters, clients, or downstream applications.
• Expose ontology data through APIs and query endpoints for applications, dashboards, and AI agents.
• Integrate data from operational systems, including ATS, VMS/MSP, credentialing, scheduling, timekeeping, and payroll platforms.
• Apply appropriate data security and privacy controls, including access rules, encryption, audit logging, and data minimization for sensitive information.
• Collaborate with the Ontology Architect to ensure data mappings, relationships, and derived logic align with the enterprise ontology.
• Document data pipelines, integrations, quality checks, operational procedures, and troubleshooting steps.
• Identify opportunities to improve pipeline reliability, data consistency, performance, and scalability.

Responsibilities

• Develop, test, deploy, and maintain reliable data ingestion and transformation pipelines.
• Integrate data from multiple SaaS and operational systems using REST APIs, webhooks, SFTP, and flat-file exports.
• Transform and map source-system records into ontology objects, properties, and relationships.
• Implement entity resolution, deduplication, and master-record matching across source systems.
• Develop and maintain data-quality validation, monitoring, error handling, and recovery processes.
• Build services and query interfaces that make ontology data accessible to downstream applications and AI-enabled workflows.
• Work with the Ontology Architect to implement derived properties and rules and validate their behavior against business requirements.
• Apply secure engineering practices for sensitive data, including appropriate access controls, encryption, auditability, and minimization.
• Troubleshoot integration failures, data discrepancies, and performance issues, and implement durable fixes.
• Collaborate with engineering and product teams to deliver dependable, maintainable data services.

Required Skills and Experience

• 4+ years of experience in data engineering, data integration, or a closely related discipline.
• At least 1 year of experience working with a graph database, ontology platform, or semantic-layer technology.
• Strong Python and SQL skills.
• Experience with Spark or PySpark and a data orchestration or transformation tool such as Airflow, dbt, or Foundry Pipeline Builder.
• Hands-on experience with at least one of the following: Palantir Foundry, Neo4j, Amazon Neptune, or an RDF triple store, including the relevant query language or tooling.
• Experience integrating SaaS and operational systems through REST APIs, webhooks, SFTP, and flat-file exports.
• Understanding of data modeling, data transformation, entity resolution, and data-quality practices.
• Experience building, monitoring, and troubleshooting production data pipelines.
• Ability to work with technical and business stakeholders to clarify requirements and resolve data issues.
• Working knowledge of secure data-handling practices, including access controls, encryption, audit logging, and data minimization.

Preferred Qualifications

• Experience working with healthcare or staffing data, including VMS feeds, UKG/Kronos, or credentialing systems.
• Experience with cloud data platforms such as AWS, Microsoft Azure, or Google Cloud Platform (GCP).
• Familiarity with infrastructure as code and automated deployment practices.
• Experience using TypeScript or React to build internal tools or applications that consume ontology data.
• Experience supporting AI applications or agents through reliable data services, graph queries, or semantic data access.

Competencies

• Strong data engineering and integration skills, with attention to reliability and maintainability.
• Analytical problem-solving and a methodical approach to diagnosing data issues.
• Attention to detail when mapping entities, relationships, and business rules.
• Ability to build robust pipelines and services that support production applications.
• Commitment to data quality, security, documentation, and operational monitoring.
• Effective communication and collaboration with architecture, engineering, product, and business teams.
• Ability to manage priorities and resolve issues in a fast-moving platform environment.

Culture Fit

You will thrive here if:

• Enjoy building dependable data pipelines and integrations that power real operational workflows.
• Take ownership of data quality and investigate issues before they affect downstream users.
• Are comfortable working across multiple source systems and evolving technical requirements.
• Value clear documentation, testing, monitoring, and secure engineering practices.
• Collaborate closely with architects and engineers to turn semantic models into working data services.
• Look for practical ways to improve automation, reliability, and platform performance.

You may not thrive here if:

• Prefer one-off data tasks over maintaining production pipelines and integrations.
• Have limited experience troubleshooting data quality or integration issues.
• Treat monitoring, testing, documentation, or security as secondary concerns.
• Prefer working in isolation rather than collaborating with architecture and application teams.

Why Join Us

Join a team building the data and semantic foundation for AI-powered workforce management. As a Knowledge Graph / Data Integration Engineer, you will help connect operational systems, maintain trusted ontology data, and enable the applications and AI workflows that depend on it.

Your work will directly influence the reliability of matching, compliance, forecasting, and intelligent automation across the platform. If you enjoy solving complex integration challenges and building production-grade data services that make connected data useful, we would love to hear from you.

Pay: $128,000.00 - $222,000.00 per year

Application Question(s):

• Do you have at least 1 year of hands-on experience with a graph database, ontology platform, or semantic layer, such as Palantir Foundry, Neo4j, Amazon Neptune, or an RDF triple store? If so, list which ones.

Experience:

• Python: 5 years (Required)
• SQL: 5 years (Required)
• Data Pipelines & Orchestration: 5 years (Required)
• API & Systems Integration: 5 years (Required)
• Data Engineering or Data Integration: 5 years (Required)

Work Location: Remote