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.

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. in data engineering, healthcare data platforms, HealthTech, or healthcare interoperability. Python and production-grade data engineering.

Apache

Spark/PySpark at scale , including partitioning, performance optimization, memory tuning, and distributed processing. ETL/ELT pipelines , performance and scalability bottlenecks in distributed data-processing systems. including resources, profiles, Bundles, validation, and large-scale healthcare data transformation. Strong understanding of healthcare terminology standards including Experience working with clinical, claims, coverage, eligibility, or comparable healthcare data models. 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. Git, CI/CD, automated testing, observability, and production operations . Strong understanding of healthcare security, privacy, and regulatory requirements,



including reusable engineering frameworks, architectural patterns, standards, and data-quality practices AI coding agents such as Claude Code or comparable agentic engineering tools particularly where correctness, cost, performance, or PHI safety are critical.

Professional

English sufficient for direct collaboration with U.large-scale FHIR data modeling and healthcare interoperability platforms .

Experience with payer data exchange, including Experience working with pharmacy, laboratory, or other specialized healthcare datasets. S. healthcare regulatory frameworks. clinical data extraction, normalization, terminology mapping, or healthcare data quality . Contributions to open-source healthcare, FHIR/HL7 communities, or relevant industry working groups. technical direction for healthcare data engineering large-scale distributed data pipelines transforming heterogeneous source data into clean, standards-conformant FHIR.

Python, Spark/PySpark, Databricks/Delta Lake , and modern data-processing technologies. Diagnose and eliminate complex performance and scalability bottlenecks across distributed data workloads. data quality, deduplication, patient/record linking, terminology normalization, and reliable idempotent processing . 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. and ensure healthcare data solutions meet applicable security, privacy, and compliance requirements. 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.

Mentor

Senior and Middle engineers through design reviews, pairing, technical guidance, and knowledge sharing. 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. technical direction for healthcare data engineering on a big platform operating at scale .

You will own some of the platform's hardest engineering problems, establish reusable frameworks and standards, and influence how an entire engineering team builds, validates, and operates healthcare data solutions. large-scale distributed data engineering, FHIR/HL7 interoperability, healthcare terminology, data quality, cloud data platforms, and AI-native engineering . You will help determine how coding agents and reusable AI capabilities can increase engineering leverage while maintaining the rigor required for production healthcare systems and PHI-sensitive data. you will set direction and mentor engineers, but your technical authority comes from remaining deeply involved in architecture, implementation, performance optimization, and production problem solving. #

📌 Lead Data Engineer (Healthcare Solution) (Álava)
🏢 Intellias
📍 Álava