Data Architect, Clinical

Prolaio•Chicago, IL

13h•$148,000

About The Position

The Data Architect will shape the backbone of Prolaio’s healthcare data ecosystem, defining and scaling the architectures that power clinical intelligence products, real‑world data pipelines, analytics, data science, clinical operations, quality, and other enterprise use cases in a regulated environment. In this role, you turn complex clinical and operational data into interoperable, trustworthy, and scalable platforms that enable Prolaio to transform continuous, predictive, and shareable heart data into faster therapeutic discovery and better cardiovascular outcomes. This role is ideal for a self‑starter with deep healthcare data experience who wants to architect modern platforms across structured, semi‑structured, and unstructured data, embedding lifecycle management, standardization, lineage, provenance, compliance, and AI enablement into the foundation of Prolaio’s data ecosystem.

Requirements

Bachelor’s degree in computer science, Data Engineering, Information Systems, Biomedical Informatics, Engineering, or a related field.
Minimum 10 years of experience in healthcare, life sciences, medtech, digital health, or other patient-data-focused environments, including at least 5 years in clinical data, real-world data, clinical trials data, or other patient-centered data platforms.
Demonstrated experience architecting healthcare data platforms that support structured, semi-structured, and unstructured data in regulated or validated environments.
Deep understanding of data from cardiology focused wearables devices, claims, socioeconomic, behavioral, and EHR.
Deep understanding of healthcare data domains, data modeling, standardization, normalization, and the application of healthcare terminologies and common data models.
Strong experience working in a cross-functional, matrixed organization and partnering effectively with Product, Engineering, Data Science, Analytics, Clinical Operations, Quality, and Security teams, with the ability to operate as a self-starter in a growth-stage environment.
Strong familiarity with cloud platforms such as GCP, AWS, or Azure and modern data platforms such as Snowflake, Databricks, BigQuery, Redshift, Synapse, or comparable environments.
Experience with ETL or ELT, orchestration, and large-scale data processing tools such as dbt, Airflow, Dagster, Spark, Kafka, Fivetran, Informatica, Talend, or similar technologies.
Strong SQL and Python skills, with familiarity in APIs, event-driven architectures, interoperability frameworks, and clinical data exchange patterns such as HL7 and FHIR
Familiarity with metadata, lineage, catalog, governance, and AI-enablement tools, including platforms for semantic indexing, vectorization, retrieval, and data lifecycle automation.

Responsibilities

Define the target-state data architecture and roadmap for Prolaio’s regulated healthcare data platform in support of product, analytics, data science, data services, clinical operations, and quality use cases.
Architect scalable, secure, and interoperable data platforms that support structured, semi-structured, and unstructured data across clinical, operational, device, and patient-generated sources relevant to Prolaio’s cardiovascular and research mission.
Lead architecture decisions for storage, compute, orchestration, metadata, APIs, and integration patterns across cloud-native environments and modern data platforms.
Establish architecture standards and reference patterns that support reliability, performance, extensibility, and validated operation in regulated settings.
Design and govern conceptual, logical, and physical data models for clinical trials, cardiovascular and physiological data, real‑world data, claims, medication, socio-economic, behavioral, and related healthcare datasets.
Apply healthcare terminologies, ontologies, and common data models where appropriate, including standards such as HL7, FHIR, ICD, SNOMED CT, LOINC, RxNorm, OMOP, and CDISC.
Lead efforts to standardize and normalize disparate data sources into governed, reusable, analysis-ready datasets suitable for regulated workflows, clinical research, and product development.
Architect end-to-end data flows and reusable pipelines for ingestion, validation, transformation, enrichment, publishing, retention, archival, and decommissioning across batch, streaming, and hybrid workloads.
Define and implement data lifecycle management practices that support retention, access control, traceability, reproducibility, lineage, provenance, and auditability in regulated healthcare environments.
Design data architectures that enable AI, analytics, and machine learning use cases by supporting feature generation, retrieval workflows, governed access, and production deployment needs.