Sr. Data Engineer - Ontology & Semantic Modeling

We’re building a modern data + AI platform where ontologies and semantic models create a consistent understanding of entities, relationships, and meaning across systems. We are seeking a Sr. Data Engineer (Ontology & Semantic Modeling) to design scalable data pipelines, contribute to an ontology-driven semantic layer, and help improve database schema and performance across our platform. Ontology & Semantic Layer Design, evolve, and maintain ontologies, taxonomies, and semantic models Define entities and relationships; create mappings from source systems into semantic structures Establish practical governance practices (versioning, documentation, naming standards) Ensure semantic consistency across pipelines, APIs, and downstream applications Data Engineering (Databricks) Build and operate scalable pipelines using Databricks Design reliable, well-structured datasets and transformation frameworks Improve data quality through validation, deduplication, and monitoring Optimize Spark and SQL workloads for performance and cost efficiency Database Architecture & Performance (Postgres) Analyze existing Postgres schemas and query patterns to identify performance bottlenecks Improve table structures, indexing strategies, and data access patterns Review query execution plans and optimize joins and filtering logic Evaluate trade‑offs between normalized, dimensional, and denormalized models Partner with engineering teams to resolve latency and scaling challenges AI & Agent Enablement Structure datasets and metadata to support LLM and agentic AI workflows Support retrieval use cases (RAG / hybrid search) by preparing clean, linked, high‑signal data Collaborate with AI teams to validate semantic consistency and correctness Cloud & Collaboration Work within AWS environments (S3, IAM, compute, managed databases) Collaborate across data engineering, AI, product, and platform teams Document architecture decisions, ontology standards, and best practices Minimum Qualifications 5+ years in Data Engineering or Data Architecture Strong experience with Databricks + Spark in production environments Advanced SQL and strong data modeling skills (conceptual/logical/physical) Strong experience with Postgres, including schema design and performance tuning Experience building reliable ETL/ELT pipelines Proficiency in Python (or Scala) for data engineering workflows Experience working in AWS environments Experience implementing data quality practices (validation, deduplication, monitoring) Nice to Have Comfortable working with ontology / semantic modeling concepts Knowledge graph concepts; exposure to RDF/OWL/SPARQL Graph databases (Neo4j, etc.) Experience supporting AI/ML or LLM‑based systems (RAG/hybrid retrieval) Experience with orchestration tools (Airflow, Dagster, dbt) Experience with governance/lineage tooling (e.g., Unity Catalog) Travel: 15% Location: Fremont, CA, Office Hybrid Workplace Employees are expected to work onsite for a minimum of 3 days per week, unless the advertised role has a specific on‑site requirement. SoundThinking provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability or genetics. In addition to federal law requirements, SoundThinking complies with applicable state and local laws governing nondiscrimination in employment in every location in which the company has facilities. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training. SoundThinking expressly prohibits any form of workplace harassment based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability or veteran status. #J-18808-Ljbffr SoundThinking