Lead Healthcare Data Engineer - FHIR & AI Platform

Intellias; España; Sur place; EUR 90 000 - 150 000; Plein temps

Résumé du poste

Intellias is seeking a Lead Healthcare Data Engineer to drive the design and delivery of a large-scale, FHIR-based health data platform. You will guide end-to-end data pipelines, enforce data quality standards, and shape reusable engineering patterns across teams in a fast-moving, PHI-sensitive environment.

You will mentor engineers, influence architecture decisions, and collaborate with clinical experts to transform fragmented data into clean, standardized information, leveraging Databricks,

Qualifications

• 8+ years of professional experience in data engineering, healthcare data platforms, HealthTech, or healthcare interoperability.
• Lead-level technical ownership of complex production systems with the ability to set direction across multiple components and teams.
• Strong hands-on expertise in Python and production-grade data engineering.
• Deep experience with Spark/PySpark at scale, including partitioning, optimization, memory tuning, and distributed processing.
• Experience with Databricks and Delta Lake or comparable lakehouse 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 performance and scalability bottlenecks in distributed data-processing systems.
• Deep hands-on experience with FHIR R4, including resources, profiles, 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 with data quality, deduplication, 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.
• Demonstrated technical leadership through architecture/design reviews, mentoring, pairing, and cross-team influence.
• Experience using AI coding agents as part of development lifecycle.
• Strong judgment on when to use AI/LLM-based approaches vs deterministic solutions, especially for PHI safety and cost/performance considerations.
• Professional English sufficient for direct collaboration with US-based stakeholders.

Responsabilités

• 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 tech.
• 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 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 to accelerate healthcare data capabilities across the team.
• Champion PHI-safe engineering practices and ensure compliance in security/privacy.
• Make AI-native engineering a practical part of development using coding agents and reusable AI capabilities.
• Establish guardrails for AI-assisted development to maintain correctness, testing, and security.
• Determine where deterministic processing outperforms AI/LLM approaches and define patterns for both.
• Mentor Senior and Middle engineers through design reviews and knowledge sharing.
• Lead complex engineering problems from investigation through architecture and production operation.
• Collaborate with stakeholders to translate interoperability requirements into scalable technical solutions.
• Represent strong healthcare data engineering practices in architecture and standards discussions.

Connaissances

Python

Spark/PySpark

Databricks/Delta Lake

ETL/ELT pipelines

FHIR R4

US Core/HL7

AI coding agents

PHI-safe engineering

Leadership

Production problem solving

Outils

Databricks

Delta Lake

Git

CI/CD

Description du poste

Intellias is seeking a Lead Healthcare Data Engineer to drive the design and delivery of a large-scale, FHIR-based health data platform. You will guide end-to-end data pipelines, enforce data quality standards, and shape reusable engineering patterns across teams in a fast-moving, PHI-sensitive environment.

You will mentor engineers, influence architecture decisions, and collaborate with clinical experts to transform fragmented data into clean, standardized information, leveraging Databricks,