Analytics Engineer

Beacon is a talent firm representing a high-growth healthcare AI and clinical data platform company. The Analytics Engineer will own the full lifecycle of data models, building transformation pipelines, enforcing quality and testing standards, and converting complex multimodal clinical data into analysis-ready datasets for AI development and patient journey analysis.

Responsibilities:

• 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

Requirements:

• 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
• 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
• Comfort in a cloud environment (Snowflake and/or AWS preferred)
• 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