Pay Growth
?
out of 5
Not enough data
Not enough info to score pay or growth
Data Engineer Architect
Job
Stella Technologies
Dayton, OH (In Person)
Full-Time
Review key factors to help you decide if the role fits your goals.
Job Security
?
out of 5
Not enough data
Calculating job security score...
Total Score
82
out of 100
Average of individual scores
Skill Insights
Compare your current skills to what this opportunity needs—we'll show you what you already have and what could strengthen your application.
Job Description
Data Engineer Architect at Stella Technologies Data Engineer Architect at Stella Technologies in Dayton, Ohio Posted in 11 days ago.
Build validation examples, test fixtures, and versioning guidance so the schema can grow iteratively as the domain model matures.
Develop a formal ontology (OWL/RDF or equivalent) that standardizes terminology and captures relationships across data objects, making the model usable across organizations.
Collaborate closely with domain SMEs to ensure the data model faithfully represents the real-world semantics, constraints, and dependencies they work with daily.
Write and maintain technical documentation, including data-model guides, schema usage references, and integration patterns, for both practitioners and future developers.
Identify and resolve data-quality issues such as duplication, inconsistency, ambiguous definitions, and gaps in the source material.
Support workflow definition by specifying the data objects consumed and produced at each step, ensuring traceability between the workflows and the underlying schema.
Ensure all deliverables are open, non-proprietary, and provided with full Government data rights, with no vendor lock-in or licensing constraints.
What We're Looking For5+ years of professional experience in data engineering, data architecture, or knowledge engineering.
Strong proficiency with JSON Schema, including experience designing schemas from scratch rather than only consuming existing ones.
Hands-on experience building or working with formal ontologies (OWL, RDF, SKOS) or controlled vocabularies in a professional setting.
Deep understanding of data-modeling fundamentals: normalization, entity-relationship design, attribute taxonomies, and schema evolution strategies.
Proven ability to collaborate with domain experts who think in documents and processes rather than data structures, and to translate their knowledge into structured, maintainable models.
Comfortable with Git, documentation-as-code workflows, and collaborative development practices.
Active Secret clearance or ability to obtain one prior to start.
Nice to HaveExperience in DoW acquisition, systems security engineering, or program protection environments.
Familiarity with Program Protection concepts such as CPI, critical functions, security classification, or Anti-Tamper, even at a general level.
Hands-on experience with knowledge-graph technologies, SPARQL, or graph databases.
Background with NIST frameworks (800-171, 800-53) or other federal information-security standards.
Prior experience building data models intended for multi-organization or cross-vendor use.
Interest in expanding into ML/AI data pipelines, digital twins, or model-based systems engineering. recblid jlnl89x4m68nym6pa479zk44ypoegt
Type:
full-timeJob Description:
What You'll DoDesign and develop JSON Schema representations of the core data objects, attributes, validation rules, and relationships that underpin the Program Protection process.Build validation examples, test fixtures, and versioning guidance so the schema can grow iteratively as the domain model matures.
Develop a formal ontology (OWL/RDF or equivalent) that standardizes terminology and captures relationships across data objects, making the model usable across organizations.
Collaborate closely with domain SMEs to ensure the data model faithfully represents the real-world semantics, constraints, and dependencies they work with daily.
Write and maintain technical documentation, including data-model guides, schema usage references, and integration patterns, for both practitioners and future developers.
Identify and resolve data-quality issues such as duplication, inconsistency, ambiguous definitions, and gaps in the source material.
Support workflow definition by specifying the data objects consumed and produced at each step, ensuring traceability between the workflows and the underlying schema.
Ensure all deliverables are open, non-proprietary, and provided with full Government data rights, with no vendor lock-in or licensing constraints.
What We're Looking For5+ years of professional experience in data engineering, data architecture, or knowledge engineering.
Strong proficiency with JSON Schema, including experience designing schemas from scratch rather than only consuming existing ones.
Hands-on experience building or working with formal ontologies (OWL, RDF, SKOS) or controlled vocabularies in a professional setting.
Deep understanding of data-modeling fundamentals: normalization, entity-relationship design, attribute taxonomies, and schema evolution strategies.
Proven ability to collaborate with domain experts who think in documents and processes rather than data structures, and to translate their knowledge into structured, maintainable models.
Comfortable with Git, documentation-as-code workflows, and collaborative development practices.
Active Secret clearance or ability to obtain one prior to start.
Nice to HaveExperience in DoW acquisition, systems security engineering, or program protection environments.
Familiarity with Program Protection concepts such as CPI, critical functions, security classification, or Anti-Tamper, even at a general level.
Hands-on experience with knowledge-graph technologies, SPARQL, or graph databases.
Background with NIST frameworks (800-171, 800-53) or other federal information-security standards.
Prior experience building data models intended for multi-organization or cross-vendor use.
Interest in expanding into ML/AI data pipelines, digital twins, or model-based systems engineering. recblid jlnl89x4m68nym6pa479zk44ypoegt