Analytics Engineer

This person will own the full lifecycle of the company’s data models - designing and implementing transformation logic, enforcing data quality and testing standards, optimizing pipeline performance, and documenting data models for discoverability across the organization. They’ll query complex source systems across a range of health data types (EMR, ECG, DICOM) to map data elements and support both AI training datasets and in-depth patient journey analyses. This role reports to the Data Science Manager, under the Chief Data Officer, and works closely with technical and clinical subject matter experts to translate research and model requirements into engineering solutions., * Query complex source systems across EMR, ECG, and DICOM data to identify and map data elements supporting AI training and patient journey analyses

• Develop and maintain data transformation pipelines using dbt and Snowflake, ensuring data quality, lineage, and transparency
• Harmonize multimodal data from multiple health systems into a unified ontological layer, with input from clinical experts
• Perform complex data extraction, manipulation, and summarization to create analytical data models
• Implement data quality tests, CI/CD, and version control best practices across the data modeling pipeline
• Support software engineers in optimizing de-identification and ETL processes across disparate health system cloud environments
• Work with NLP experts to structure and model discrete clinical concepts abstracted from unstructured text
• Collaborate with technical and clinical SMEs and customers to translate research and model requirements into engineering solutions

• Bachelor’s degree in a quantitative field (Data Science, Biomedical Informatics, Computer Science, Biostatistics)
• 3+ years in analytics or data engineering roles with hands-on cleaning and structuring of clinical/EHR data
• Strong dbt and SQL proficiency: reusable Jinja macros, custom tests, multi-environment deployments
• 1+ years extracting, curating, and analyzing HIT/healthcare delivery data (EMR, claims, registry); familiarity with FHIR, CDA, CQL, and clinical terminology standards (ICD, CPT, LOINC, SNOMED-CT, NDC, RxNorm) a plus
• Comfort in a cloud environment (Snowflake and/or AWS preferred)
• Proficiency with Git and version control workflows
• Advocate for software engineering best practices (modularity, unit testing, clean documentation) within a data science team
• Comfortable with ambiguity in a fast-paced, early-stage startup environment
• Excellent communication skills, able to translate customer needs into data solutions

Nice to Have: LLM/RAG integration experience (Snowflake Cortex, Bedrock, Azure OpenAI), Python for data cleaning/feature engineering, exposure to AI/ML modeling teams, Epic/Cerner/Allscripts familiarity, medical ontology or DICOM/imaging experience, OMOP common data model, agile tooling (Jira/Linear), dbt Cloud

Pulled from the full job description

• Health insurance
• Paid time off
• Vision insurance
• Dental insurance
• Flexible schedule, * Remote work and flexible hours, with meetings kept to a healthy minimum
• Comprehensive wellness benefits: healthcare, dental, vision, PTO, sick days
• Professional development days
• Collegial, academically-minded culture with startup speed and flexibility
• Strong balance of focus time and easy access to collaborators
• Mission-driven company focused on improving patient care through AI

Beacon Talent has been retained by a high-growth healthcare AI company to identify an Analytics Engineer to join their growing data team. This is an opportunity to own the transformation layer of a modern ELT pipeline turning messy, multimodal clinical data into clean, analysis-ready datasets that power responsible clinical AI development.

About the Client

Our client is a mission-driven healthcare AI and clinical data platform company, founded in 2020 by experts in health tech, hospital systems, academia, and clinical AI. They are building the world’s largest AI training and validation platform, partnering with major U.S. health systems to safely and ethically make de-identified patient data available to AI developers. Their platform spans over 15 million patients and includes structured EMR data, unstructured clinical text, imaging, video, and waveform/streaming inpatient data dating back to 2016. They are an AWS and Python shop, and their culture centers on learning from data to help clients improve health outcomes through AI.