Staff Data Engineer

• Intellias is looking for a Staff Healthcare Data Engineer to join a large-scale digital healthcare initiative focused on building a FHIR-based health data platform and transforming fragmented healthcare data into clean, standardized, and trustworthy information.
• This is a Staff-level Individual Contributor role combining deep healthcare data engineering expertise, distributed data systems, FHIR interoperability, and AI-native software development. You will set technical direction for how healthcare data is ingested, transformed, validated, and delivered at scale while solving the platform’s most complex engineering and data-quality challenges.
• The role requires both deep hands-on engineering expertise and broad technical influence — establishing reusable frameworks and standards, mentoring experienced engineers, and raising the engineering bar across the team.
• The project is building a FHIR-based healthcare data platform at scale, integrating information from EHRs, healthcare organizations, payer systems, national and reference datasets, and other clinical data sources.
• The platform transforms heterogeneous healthcare data into clean, standards-conformant FHIR while addressing complex challenges around data quality, patient and record matching, deduplication, terminology normalization, distributed processing, and reliable delivery at production scale.
• The technology landscape includes Python, Apache Spark/PySpark, Databricks, Delta Lake, SQL, FHIR R4, HL7, C-CDA, healthcare terminology standards, and modern cloud-native data infrastructure.
• This is an AI-native engineering environment where coding agents such as Claude Code and reusable AI capabilities are integrated into the engineering lifecycle. AI is used to accelerate development and solve appropriate healthcare data problems while maintaining strict standards for correctness, deterministic processing, testing, security, and PHI safety.

Requirements:

• 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.

Nice to Have

• Deep experience with large-scale FHIR