Data Integration Engineer

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 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:

• 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.

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)