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Company

Particle Health · 6 hours ago

Healthcare Data Architect / Sr. Data Engineer

United States

Full-time

Remote

Senior Level, Lead/Staff

8+ years exp

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Developer APIsElectronic Health Record (EHR)

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Responsibilities

Build and optimize efficient, scalable data pipelines for ingestion, transformation, and enrichment, powering Particle Health’s Platform.

Work with product and engineering leadership to develop product features and analyses that enhance our understanding of the data and its applications.

Monitor, troubleshoot data issues, and ensure data availability, reliability, and scalability for the SaaS product.

Lead the design and development of a robust data architecture that guides data modeling, ingestion, processing, and data quality enabling data product development at Particle Health.

Design and develop conceptual, logical, and physical data models ensuring they meet the needs of our platform and customers and accounting for the data domains, structures, relationships, quality, security, and data privacy of the product.

Document and communicate architectural designs and decisions to stakeholders across Product, Engineering, and Senior Leadership.

Champion initiatives, addressing gaps in our data, identifying solutions, and improving reliability to establish a competitive edge and firmly cementing our data quality as a key differentiator of our platform.

Define and implement data standards, policies, procedures, and best practices for data management, data quality, data security, and data privacy, ensuring compliance with internal and external regulations and standards.

Define and maintain standards for data usage, access control, and compliance with healthcare regulations.

Establish and enforce best practices for managing our data sets, ensuring updates align with Data Usage Agreements.

Work cross-functionally with Data Science, Product, and Engineering teams to align on architectural needs and enable the development of new features and analyses.

Partner with leadership to translate business requirements into scalable data solutions that provide insight and impact.

Qualification

Find out how your skills align with this job's requirements. If anything seems off, you can easily click on the tags to select or unselect skills to reflect your actual expertise.

Healthcare dataData architectureData modelingData engineeringSQLPythonSparkData governanceData pipelinesCloud environmentData modeling toolsBig data technologiesData lake technologiesReal-time streamingBatch processing

Required

Extensive experience with healthcare data (especially EHR and / or Claims data, ADTs), including clinical applications and / or risk stratification.

Proven proficiency in building and optimizing data-intensive systems and pipelines.

Strong technical experience - we use SQL & Python, specifically Spark.

Deep curiosity and critical thinking to explore and break down complex data sets to isolate key outcomes.

Effective communication skills to convey complex technical concepts to both technical and non-technical stakeholders.

Experience developing data-intensive applications and reliable data pipelines in a cloud environment.

Ability to understand and prioritize work based on value and goals of a project.

Analytical thinking and interest in using metrics to improve decision-making and influence behavior.

Comfort with ambiguity.

A lifelong learner who has the demonstrated ability to pick up new skills quickly.

8+ years of experience in data engineering with a strong background in data architecture and data modeling.

5+ years of experience in data architecture, data modeling, data governance, and data analytics, preferably for SaaS products or cloud-based applications.

Experience with healthcare data & formats (e.g. CCDA, FHIR, HL7v2, ADTs, Tuva Schema, OMOP).

Strong knowledge and skills in data modeling tools and languages, such as ERD, UML, SQL, and NoSQL, and in designing data models for SaaS products or cloud-based applications.

Familiar with data governance frameworks, standards, and best practices.

Experience in working with various data platforms and systems, such as relational databases, data warehouses, data lakes, cloud services, and big data technologies, and with selecting and using the appropriate data platforms and systems for SaaS products or cloud-based applications.

Deep Expertise in Python, with a deep understanding and hands-on experience in Spark for designing, optimizing, and scaling large-scale data processing pipelines, and proficiency in at least one SQL dialect.

Experience with data lake technologies (e.g., Databricks, Delta Lake), data storage formats (Parquet, Avro), query engines (such as Photon, Spark SQL), and both real-time streaming and batch processing, or equivalent technologies and frameworks.