Intellias is looking for a
Lead 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.
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.
Project Overview:
We're helping a US digital-health platform get its patients' medical data in order: lab results, diagnoses, medications, visits and more. The data comes from many different clinics and labs, each with its own format, so right now it's pretty messy. Our job is to clean up and standardize that data close to the source, bring scattered logic into one place, and see the real data quality every day instead of guessing. Data engineers will work mostly in Databricks/Spark, building pipelines, reference data and quality checks together with the dev team and clinical experts.
Requirements:
8+ years of professional experience
in data engineering, healthcare data platforms, HealthTech, or healthcare interoperability.
Proven
Lead-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 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 clin

📌 Lead Data Engineer (La Coruña)
🏢 Intellias
📍 La Coruña