Data Engineer (Healthcare)

Prime Health Technologies is redefining healthcare with its AI-driven Precision Health Operating System aimed at improving population health outcomes. The Data Engineer will design and operate the platform's data infrastructure, ensuring reliable data flows and compliance with regulations. This hands-on role requires building data pipelines and supporting MLOps as the platform matures.

Responsibilities:

• Build & maintain reliable batch and (where appropriate) streaming pipelines for clinical, operational, product, and third-party data sources, including healthcare & consumer-health integrations: HL7/FHIR, REST APIs, Apple HealthKit, Android Health Connect, and governed adapters for external clinical or wellness sources
• Design data models, transformations, and storage patterns supporting analytics, reporting, AI workloads, and product features — with reproducibility as a first-class requirement (any curated dataset must rebuild deterministically from raw inputs & transformation code)
• Design & operate core stores in the in-country PHI data plane (operational database, time-series store, object storage, audit logs) with encryption, access control, and lifecycle management
• Build curated, de-identified-by-default analytics datasets powering operational, regulatory, and client dashboards
• Implement & maintain PHI/PII de-identification & tokenization pipelines; support tightly controlled re-identification workflows when explicitly authorized
• Establish data quality, integrity, and observability controls (validation, reconciliation, idempotency, late-arriving data handling, lineage, monitoring, alerting) and publish quality metrics
• Deliver a discoverable metadata layer so teams can self-serve and trust datasets
• Support sovereign / regional data-residency models, keeping PHI within an approved deployment boundary while enabling derived & aggregate views in out-of-country planes
• Own pipeline observability — logging, metrics, tracing, alerting, cost & performance tuning — across the stack
• Contribute to CI/CD for data components & participate in incident response and postmortems
• Partner with engineering, product, clinical, and business stakeholders translating data needs into scalable technical solutions
• Training-job orchestration & reproducible dataset versioning
• Model registry & artifact storage
• Containerized model serving, routing, and shadow-deployment infrastructure
• Inference logging back into the warehouse for downstream evaluation
• CI/CD for model artifacts (schema validation, contract tests, automated rollouts)

Requirements:

• 7+ years in data engineering or backend engineering with significant data-pipeline ownership; substantial seniority is expected given the regulated, national-scale, and sovereign-deployment context. Prior work in healthcare, wellness, insurance, or other regulated domains
• Strong SQL & Python; proven track record building reliable ETL/ELT pipelines in production
• Experience with modern storage patterns: operational databases, data lakes / object storage, and analytics warehouses or lakehouses
• Hands-on experience with orchestration tools (Airflow, Dagster, Prefect, or equivalent) and transformation frameworks (dbt or equivalent)
• Demonstrated discipline around data contracts, schema evolution, and reproducible pipelines (deterministic rebuilds from raw + code)
• Experience working with sensitive data (PII/PHI), implementing least-privilege access patterns, audit logging, and consent-aware data access
• Familiarity with data classification, retention, deletion, and auditability requirements for sensitive data
• Experience with data quality & observability practices: validation/testing, lineage/metadata, monitoring/alerting, incident response
• Clear written & verbal communication; able to produce data documentation, runbooks, and pragmatic design proposals
• Experience supporting audits & control evidence in ISO 27001-aligned environments; familiarity with ISO 42001 AI governance expectations & privacy regimes such as HIPAA & GDPR
• Exposure to HL7/FHIR or common clinical code sets (ICD, SNOMED) and the realities of integrating heterogeneous health datasets
• Prior work in data-residency / sovereign-cloud environments with split-plane architectures (in-country PHI plane plus out-of-country derived/aggregate views)
• Experience with time-series databases & high-volume sensor / wearable data pipelines
• Growth direction — MLOps: experience extending data platforms with ML infrastructure (training orchestration, model registries, feature-pipeline runtimes with batch-to-online parity, containerized serving, inference logging). Candidates who have collaborated closely with data science teams and have intuition for what makes good scaffolding for that workflow are particularly valuable, since this role will grow into MLOps as the platform matures toward pilots