Past JobThe Sr. Data Engineer designs, builds, and maintains the pipelines, graph, and semantic data models that form the Xenter Data Layer - the cloud platform where human clinical needs meet cutting-edge engineering. This role moves device and clinical data - including GURU procedure data and Xenter Diagnostics assessment results - from the edge into a governed, FHIR-native property graph enriched by a medical ontology, so that clinical decision-support tools, analytics, machine-learning models, and AI agents can reason over trustworthy, interconnected data. This is not a traditional data-warehouse role. Our platform is built on a knowledge-graph and semantic foundation: a Postgres property graph for patient data, an RDF/OWL ontology (SNOMED CT, RxNorm, LOINC) with a reasoner for clinical inference, and an LLM-agentic layer that queries both. We are looking for an engineer who is genuinely excited by graph and semantic data - not only tables and rows. Essential Duties and Responsibilities • Design, build, and maintain ingest pipelines that move data from GURU and Xenter Diagnostics devices into the cloud data layer, using Temporal durable workflows and a NATS JetStream event spine. • Model clinical and device data as a FHIR R4-aligned property graph - designing node types, edges, resolution keys, and content-addressed, versioned class schemas that downstream tools and models depend on. • Extend and maintain the medical ontology (RDF/OWL in Apache Jena/Fuseki - SNOMED CT, RxNorm, LOINC, clinical guidelines) and the cross-graph joins that let a reasoner answer clinical questions (e.g., contraindication and subsumption queries) over patient data. • Write and optimize SQL (Postgres 16 / TimescaleDB) and SPARQL, and author Python for data transformation, validation, and pipeline logic. • Build monitoring and data-quality checks - declared in signed ingest contracts and instrumented with structured logging, OpenTelemetry, and Prometheus - to catch issues before they reach downstream consumers. • Handle high-frequency time-series and device-waveform telemetry (TimescaleDB hypertables, EMQX/MQTT) with the same rigor as structured clinical records. • Coordinate with software engineering on schema design for new data sources, and provide data-science, analytics, and ML/AI-agent stakeholders with well-documented, trustworthy, query-ready datasets and graph surfaces. • Uphold the platform's PHI protection boundary - tokenized fields, purpose-of-use authorization (SpiceDB), and the external PHI Vault - so plaintext PHI never persists in data stores. • Support the evolution of the data infrastructure as data volume grows with company scaling and expanding device fleets. Required Education and Experience • 5+ years building data pipelines in a production environment. • Strong SQL skills and strong Python for data engineering. • Hands-on experience with graph data and/or semantic/ontology technologies - property graphs, RDF/OWL, knowledge graphs, SPARQL, or graph query languages - and a real interest in modeling data as interconnected entities rather than flat tables. • Solid data-modeling ability, including entity/relationship modeling and schema design (dimensional and normalized modeling understood as background, not the primary paradigm here). • Experience delivering on a cloud platform (Azure preferred) and with containerized / Kubernetes-based services. • Working knowledge of healthcare data standards - HL7 / FHIR. • Comfort operating in regulated, HIPAA-governed data environments. • Experience with version control (Git) and collaborative engineering workflows. Strongly Preferred • Experience with ontologies and reasoners - OWL, description logics, subsumption / inference, or clinical terminologies (SNOMED CT, RxNorm, LOINC). • Familiarity with LLM / agentic platforms - retrieval over graphs, tool-using agents, RAG, or agent frameworks - and an understanding of how AI agents consume structured and semantic data. • Streaming and time-series engineering, including device-waveform data (e.g., cardiovascular / hemodynamic signals). • Workflow orchestration with Temporal (or comparable durable-workflow / event-driven systems; Airflow / Dagster background is transferable). • Event-driven architecture on NATS JetStream, Kafka, or similar. • FHIR R4 modeling in production. • Exposure to fine-grained authorization (SpiceDB / ReBAC), secrets management (HashiCorp Vault), and PHI tokenization or de-identification. • Familiarity with the ML lifecycle (MLflow, ONNX) as a data provider to model training and serving. Physical Demands and Work Environment This position operates in a professional office environment and routinely uses standard office equipment such as computers and monitors. This is largely a sedentary role involving extended periods of computer use; some tasks may require the ability to move within an office setting. Other Duties This job description is not designed to cover or contain a comprehensive listing of activities, duties, or responsibilities required of the employee for this job. Duties, responsibilities, and activities may change at any time, with or without notice. Description here