Note: The job is a remote job and is open to candidates in USA. Intellias is seeking a Staff Healthcare Data Engineer to support a large-scale digital healthcare initiative. The role will set technical direction for a FHIR-based health data platform, owning distributed data pipelines, healthcare interoperability, data quality, validation, and production-scale delivery. It also involves mentoring engineers and establishing AI-native engineering practices while maintaining correctness, security, and PHI safety.
Responsibilities
- Set the technical direction for healthcare data engineering across ingestion, transformation, validation, normalization, and delivery
- Own the end-to-end architecture of large-scale distributed data pipelines transforming heterogeneous source data into clean, standards-conformant FHIR
- Design and evolve scalable solutions using Python, Spark/PySpark, Databricks/Delta Lake, and modern data-processing technologies
- Diagnose and eliminate complex performance and scalability bottlenecks across distributed data workloads
- Establish standards and reusable frameworks for data quality, deduplication, patient/record linking, terminology normalization, and reliable idempotent processing
- Define architectural patterns for FHIR transformation, validation, schema evolution, and production delivery
- Build and evolve automated testing and validation capabilities for data quality and FHIR conformance
- Establish engineering patterns and reusable tooling that allow the broader team to deliver new healthcare data capabilities faster and more consistently
- Champion PHI-safe engineering practices and ensure healthcare data solutions meet applicable security, privacy, and compliance requirements
- Make AI-native engineering a practical part of the team's development model, using coding agents and reusable AI capabilities to automate repetitive work and accelerate complex engineering tasks
- Establish engineering guardrails for AI-assisted development, ensuring AI-generated solutions remain correct, tested, secure, observable, and PHI-safe
- Determine where deterministic processing provides better reliability than AI/LLM-based approaches and establish appropriate architectural patterns for both
- Mentor Senior and Middle engineers through design reviews, pairing, technical guidance, and knowledge sharing
- Drive complex, ambiguous engineering problems from initial investigation through architecture, implementation, and production operation
- Collaborate with engineering, product, security, data, and healthcare-domain stakeholders to translate complex interoperability requirements into scalable technical solutions
- Represent strong healthcare data engineering practices in relevant architecture, standards, and technical discussions
Skills
- • 8+ years of professional experience in data engineering, healthcare data platforms, HealthTech, or healthcare interoperability
- • Proven Staff-level technical ownership of complex production systems, including the ability to set technical direction and solve problems spanning multiple components and engineering teams
- • Strong hands-on expertise in Python and production-grade data engineering
- • Deep experience with Apache Spark/PySpark at scale , including partitioning, performance optimization, memory tuning, and distributed processing
- • Experience with modern lakehouse technologies such as Databricks and Delta Lake , or comparable platforms
- • Strong experience designing and operating large-scale ETL/ELT pipelines , including CDC, incremental/delta processing, idempotency, schema evolution, and batch/streaming workloads
- • Proven experience diagnosing and eliminating complex performance and scalability bottlenecks in distributed data-processing systems
- • Deep hands-on experience with FHIR R4 , including resources, profiles, Bundles, validation, and large-scale healthcare data transformation
- • Strong knowledge of US Core, USCDI, HL7 v2.x , and C-CDA-to-FHIR transformation patterns
- • Strong understanding of healthcare terminology standards including SNOMED CT, LOINC, and RxNorm
- • Experience working with clinical, claims, coverage, eligibility, or comparable healthcare data models
- • Strong experience with data quality, deduplication, record/patient linking, terminology normalization, and validation
- • Strong SQL skills and understanding of data modeling, schema evolution, data quality, and production data architecture
- • Experience with Git, CI/CD, automated testing, observability, and production operations
- • Strong understanding of healthcare security, privacy, and regulatory requirements, including HIPAA and PHI-safe engineering practices
- • Proven ability to establish reusable engineering frameworks, architectural patterns, standards, and data-quality practices adopted by other engineers
- • Demonstrated technical leadership through architecture/design reviews, mentoring, pairing, and cross-team engineering influence
- • Practical experience using AI coding agents such as Claude Code or comparable agentic engineering tools as an integral part of the development lifecycle
- • Strong engineering judgment around when to use AI/LLM-based approaches versus deterministic rule-based solutions , particularly where correctness, cost, performance, or PHI safety are critical
- • Professional English sufficient for direct collaboration with U.S.-based technical and business stakeholders
- • Deep experience with large-scale FHIR data modeling and healthcare interoperability platforms
- • Experience with Master Patient Index (MPI), patient matching, identity resolution , or large-scale record linkage
- • Experience with payer data exchange, including CARIN Blue Button , Da Vinci implementation guides, or similar standards
- • Experience working with pharmacy, laboratory, or other specialized healthcare datasets
- • Knowledge of ICD-10, CPT, CVX , and additional clinical terminology/code systems
- • Experience with 21st Century Cures Act, ONC certification, CMS Interoperability Rule , or related U.S. healthcare regulatory frameworks
- • Experience building LLM/agent-based capabilities for clinical data extraction, normalization, terminology mapping, or healthcare data quality
- • Experience designing reusable AI skills or agentic workflows adopted across engineering teams
- • Contributions to open-source healthcare, FHIR/HL7 communities, or relevant industry working groups
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