Data Engineer Architect

Rain Stella Technologies is a lean startup that delivers model based systems engineering and digital cyber engineering solutions. They are seeking a Lead Data Engineer to build the data architecture for the Program Protection process, transforming domain knowledge into structured, machine-readable data models through collaboration with domain experts.

Responsibilities:

• Design 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

Requirements:

• 5+ 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
• Experience 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