Ontology Subject Matter Expert (TS/SCI)

Vantor•Herndon, VA

1d•$165,000 - $242,000•Onsite

About The Position

Vantor is seeking a Data Engineering Subject Matter Expert (SME) to join our team. In this position you will provide technical leadership for the design, implementation, and optimization of enterprise data engineering solutions supporting Object-Based Intelligence (OBI) mission requirements, ontology-driven data integration, and advanced analytics. The Data Engineering Subject Matter Expert serves as the senior technical authority for developing and implementing scalable data architectures, pipelines, and integration frameworks that enable the ingestion, transformation, management, and delivery of structured, semi-structured, and unstructured data. This position applies data engineering best practices to support enterprise ontologies, knowledge graphs, Semantic Web technologies, and AI/ML applications while ensuring data quality, governance, interoperability, and security across diverse mission systems. Working closely with ontology engineers, software engineers, database engineers, data scientists, and Government stakeholders, the Data Engineering SME develops modern data solutions that support mission analytics, knowledge discovery, and decision advantage.

Requirements

Must be a U.S. citizen and have an active TS/SCI with CI polygraph.
12 years of experience and an advanced degree or 17 years of experience with a bachelor's degree.
Demonstrated experience designing and implementing enterprise-scale data engineering solutions and data integration architectures.
Experience developing ETL/ELT pipelines using modern data engineering tools and frameworks.
Experience integrating structured, semi-structured, and unstructured data from heterogeneous enterprise data sources.
Experience with relational databases, NoSQL databases, graph databases, and distributed data processing platforms.
Knowledge of data modeling, metadata management, data governance, and enterprise data architecture principles.
Familiarity with Semantic Web technologies, knowledge graphs, or ontology-based data integration.
Experience supporting cloud-based or containerized data platforms, including Kubernetes and OpenShift.
Ability to communicate complex technical concepts effectively to both technical and non-technical stakeholders.

Nice To Haves

Experience supporting Object-Based Intelligence, Intelligence Community, or Department of Defense mission systems.
Experience developing enterprise data lakes, lakehouses, or data fabric architectures.
Experience with distributed data processing frameworks such as Apache Spark, Kafka, NiFi, Airflow, or equivalent technologies.
Experience implementing data governance, master data management, metadata management, and data lineage solutions.
Familiarity with AI/ML data engineering workflows, feature engineering, and semantic enrichment techniques.
Experience integrating RDF triple stores, knowledge graphs, and semantic reasoning platforms into enterprise data architectures.
AWS Certified Data Engineer – Associate, Google Professional Data Engineer, Microsoft Certified: Azure Data Engineer Associate, Certified Data Management Professional (CDMP), or equivalent certification.
Experience providing technical leadership, mentoring, and formal knowledge transfer to Government personnel or large multidisciplinary teams.

Responsibilities

Lead the design, development, and implementation of enterprise data engineering solutions supporting Object-Based Intelligence mission requirements.
Design and optimize scalable data pipelines for ingesting, transforming, validating, and integrating data from multiple internal and external sources.
Develop data integration frameworks that support enterprise ontologies, knowledge graphs, and Semantic Web technologies, including RDF, OWL, and SPARQL.
Collaborate with ontology engineers, software engineers, database engineers, analysts, and Government stakeholders to translate mission requirements into scalable data engineering solutions.
Design and implement data architectures supporting cloud-native, distributed, and hybrid computing environments.
Establish data quality, metadata management, lineage, and governance processes to ensure consistency, traceability, and interoperability across enterprise data assets.
Optimize data processing performance using modern distributed processing frameworks and scalable storage technologies.
Support AI/ML initiatives by developing reliable, high-quality data pipelines that provide curated datasets and semantic context for model training, inference, and decision support.
Develop automated monitoring, validation, and testing capabilities to ensure data accuracy, completeness, and operational reliability.
Produce and maintain technical documentation, data flow diagrams, interface specifications, data dictionaries, and engineering standards.
Provide technical leadership, mentoring, and knowledge transfer activities to Government personnel and project team members regarding data engineering best practices, modern data architectures, and enterprise integration strategies.