Data Ontology Engineer

Atlas is seeking a Senior Data & Ontology Engineer to design, build, and evolve canonical data models that represent identities, assets, entities, and their relationships across large, complex enterprise environments. This role is foundational to enabling reliable analytics, automation, and machine-learning–driven decision systems.

This position is not focused on reporting or visualization
. Instead, it emphasizes semantic correctness, entity consistency, and durable data structures that downstream systems can reason over with confidence. The ideal candidate brings strong computer science fundamentals, deep experience in data modeling, and comfort operating in environments where data is incomplete, inconsistent, and constantly evolving.

• Bachelor's or Master's degree in Computer Science or a related technical field.
• 8+ years of experience in data modeling, data engineering, or schema design roles.
• Strong understanding of identity modeling, entity relationships, and normalization strategies.
• Ability to reason through ambiguity, inconsistency, and edge cases in real-world data.

• Experience with ontologies, knowledge graphs, or semantic data models.
• Experience supporting machine learning or automated decision systems.
• Familiarity with graph-oriented or relationship-centric data representations.
• Background working in security, identity, or large-scale enterprise platforms.

• Design and maintain canonical data models representing identities, assets, systems, services, and their relationships.
• Define entity schemas that balance expressiveness, performance, and long-term maintainability.
• Establish modeling standards that enable consistent interpretation across analytics, automation, and ML systems.
• Explicitly model relationships, hierarchies, and associations rather than relying on implicit joins.

• Build and maintain entity resolution, aliasing, and normalization logic across multiple source systems.
• Handle ambiguous identity mappings, duplicates, and partial matches using defensible, explainable logic.
• Design mechanisms to maintain stable identifiers across system boundaries and over time.
• Ensure entity representations remain consistent as source systems and attributes change.
• Partner with data engineering teams to implement ingestion and transformation pipelines that preserve semantic meaning.
• Ensure data models support both batch and near-real-time use cases.
• Collaborate with platform and automation engineers to enable operational data consumption.
• Identify, troubleshoot, and remediate data quality issues that impact downstream reasoning or automation.

• Collaborate with ML engineers to ensure data models are consumable by decision and classification systems.
• Define features and representations that preserve entity context and behavioral meaning.
• Assess how modeling decisions impact model accuracy, confidence, and stability.
• Iterate on models as decision systems mature and requirements evolve.

• Document schemas, relationships, assumptions, and modeling tradeoffs clearly and thoroughly.
• Establish processes to evolve data models without breaking downstream dependencies.
• Participate in architectural reviews and long-term data strategy discussions.
• Serve as a technical authority on entity modeling and semantic data design.