Job description

Our Team Dandelion Health unlocks a layer of unexplored clinical depth that has been previously inaccessible at scale. By combining all the raw multimodal data from community health systems with validated AI models, we deliver regulatory-grade scientific insights and products that give teams the evidence to design better trials, enroll the right patients, and generate AI-powered insights that drive confident decisions across the development and commercial lifecycle. Your Role You are an experienced analytics engineer who knows your way around clinical and electronic health record data. Your primary responsibility is to build, maintain, and optimize the transformation layer of our ELT pipeline, turning raw, messy clinical data into clean, well-modeled, analysis-ready datasets that our clients and internal teams can trust. You will own the full lifecycle of our data models: designing and implementing transformation logic, enforcing data quality and testing standards, optimizing pipeline performance, and documenting data models so they're discoverable and understandable across the organization. You will use your data expertise, programming abilities, and critical thinking skills to support our technical product team in scaling and growing our ability to provide meaningful data. Your team’s ultimate goal is to deliver the highest-quality data possible to our clients, who are building products that improve patient health. You will report to the Data Science Manager, under the Chief Data Officer. In this role, you will be responsible for: - Querying complex source systems in a range of health data sources (e.g., EMRs, ECG data, DICOM data) to identify and map data elements in order to create high-quality datasets to support training AI algorithms and conducting in-depth analyses of patient journeys - Developing and maintaining data transformation pipelines using dbt and Snowflake, ensuring data quality, lineage, and transparency. - Harmonizing multimodal data from multiple health systems into a unified ontological layer ready for use by data scientists and AI developers, with input from clinical experts across the company. - Performing complex data extraction, manipulation, and summarization of large databases to create analytical data models; - Implementing data quality tests, CI/CD and version control best practices for analytics codebases throughout the data modeling pipeline. - Supporting software engineers in optimizing processes and technical solutions for de-identification and ETL of clinical data from disparate health system cloud environments to Dandelion Health’s data warehouse. - Working with experts in natural language processing (NLP) to structure and model discrete clinical concepts data that have been abstracted from unstructured and semi-structured text data. - Collaborating with technical and clinical subject matter experts and Dandelion customers to translate research and model requirements into engineering solutions. You are not afraid to dig into massive, confusing, disorganized new datasets and get them under control. You are excited to learn new environments, languages, and skills. This is a small, early stage company with enormous ambitions and everyone pitches in across the team. Qualifications Required - Bachelor’s degree in a quantitative field (ex. Data Science, Biomedical Informatics, Computer Science, Biostatistics) - 3+ years of experience in analytics or data engineering roles where you are responsible for hands-on cleaning and structuring clinical and electronic health record data - Strong dbt and SQL proficiency: writing reusable Jinja macros, implementing custom tests, and comfortable with multi-environment deployments (dev/staging/prod) - 1+ years of experience with extracting, curating, and analyzing data created within the HIT and healthcare delivery ecosystem (e.g., EMR, claims, registry); this may include knowledge of the roles of data exchange and content standards (e.g., FHIR, CDA, CQL) and clinical terminology standards (e.g., ICD, CPT, LOINC, SNOMED-CT, NDC, RxNorm) - Comfort working in a cloud environment (ideally Snowflake and/or AWS). - Proficiency with Git and version control workflows - Is an advocate for bringing software engineering best practices (modularity, unit testing, concise and well-documented code) to a data science team. - Comfortable with ambiguity and creative problem-solving in a fast-paced, rapidly growing startup environment. - Excellent communication skills to advocate for the value of robust analytics engineering solutions and translate customer needs into data solutions. - Passion for improving healthcare and building infrastructure that makes research and clinical AI safer, faster, and more reliable. Nice to Have - Practical experience integrating LLM tools (e.g., Snowflake Cortex, Amazon Bedrock, Azure OpenAI Service) and frameworks (RAG, agent workflows) into data pipelines and product - Proficiency in Python for data cleaning and model feature engineering. - Experience working with AI/ML modeling teams and a conceptual grasp of cloud model deployments and evaluation. - Familiarity with the data aspects of electronic medical records, ex. Epic, Cerner, Allscripts - Any medical ontology experience - Any experience working with DICOM or other imaging modalities - Experience with OMOP common data model - Comfortable with agile tools such as Jira, Linear. - Familiarity using dbt cloud Nature of our work Our work is fast paced and iterative. We are growing, and we want to support our team members to grow in their skills as well. We are building a team that approaches problems with a diversity of perspectives, values experimentation, and refining our approach based on that experimentation. We work with the full spectrum of healthcare data from tabular data, videos, images, waveforms, etc. If a health system collects it, we might work with it! If this looks like a partial fit, please reach out, we would love to share more about the work we do for you to understand if it would be a good fit for you. There is occasional travel for in-person company working days on roughly a quarterly basis. Team Benefits - Remote work and flexible hours. Availability needed for meetings, which we try to keep to a healthy minimum - Complete wellness benefits including healthcare, dental, vision, PTO, sick days and more. Ask for details - Professional development days to build your skills - Collegial work environment - Academic bent towards inquiry and problem solving but start-up speed and flexibility - Great balance of focus time to work on projects but easy to access team members to discuss issues and work collaboratively - Dandelion is a mission-driven company that is focused on improving patient care

Requirements

Must-haves

In this role, you will be responsible for:
Developing and maintaining data transformation pipelines using dbt and Snowflake, ensuring data quality, lineage, and transparency.
Harmonizing multimodal data from multiple health systems into a unified ontological layer ready for use by data scientists and AI developers, with input from clinical experts across the company.
Performing complex data extraction, manipulation, and summarization of large databases to create analytical data models;
Implementing data quality tests, CI/CD and version control best practices for analytics codebases throughout the data modeling pipeline.
Supporting software engineers in optimizing processes and technical solutions for de-identification and ETL of clinical data from disparate health system cloud environments to Dandelion Health’s data warehouse.
Working with experts in natural language processing (NLP) to structure and model discrete clinical concepts data that have been abstracted from unstructured and semi-structured text data.
Collaborating with technical and clinical subject matter experts and Dandelion customers to translate research and model requirements into engineering solutions.
Bachelor’s degree in a quantitative field (ex. Data Science, Biomedical Informatics, Computer Science, Biostatistics)
3+ years of experience in analytics or data engineering roles where you are responsible for hands-on cleaning and structuring clinical and electronic health record data
Strong dbt and SQL proficiency: writing reusable Jinja macros, implementing custom tests, and comfortable with multi-environment deployments (dev/staging/prod)
Comfort working in a cloud environment (ideally Snowflake and/or AWS).

Nice-to-haves

Practical experience integrating LLM tools (e.g., Snowflake Cortex, Amazon Bedrock, Azure OpenAI Service) and frameworks (RAG, agent workflows) into data pipelines and product
Proficiency in Python for data cleaning and model feature engineering.
Experience working with AI/ML modeling teams and a conceptual grasp of cloud model deployments and evaluation.
Familiarity with the data aspects of electronic medical records, ex. Epic, Cerner, Allscripts
Any medical ontology experience
Any experience working with DICOM or other imaging modalities
Experience with OMOP common data model
Comfortable with agile tools such as Jira, Linear.
Familiarity using dbt cloud
Nature of our work
If this looks like a partial fit, please reach out, we would love to share more about the work we do for you to understand if it would be a good fit for you.
There is occasional travel for in-person company working days on roughly a quarterly basis.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aici/cdsqlawsllmazurepythonmlagilejira

Platform: ashby
Effort: quick

Benefits

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Dental

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Pto

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Professional Development