Senior Data Engineer, Health & Bioinformatics

This is a fully remote opportunity

Gradient AI: 

Gradient AI is a leading provider of AI solutions for the Group Health and P&C insurance industries. Our solutions improve loss ratios and profitability by predicting underwriting and claim risks with greater accuracy, as well as reducing quote turnaround times and streamlining expenses through intelligent automation. Gradient AI's SaaS platform leverages a vast industry data lake comprising tens of millions of policies and claims, providing insurers with high-resolution, data-driven insights. Customers include some of the most recognized insurance carriers, MGAs, MGUs, TPAs, risk pools, PEOs, and large self-insured employers across all major lines of insurance. Founded in 2018, Gradient has experienced strong growth every year and recently raised $56 million in Series C funding from top Insurtech investors.   

Role: 

We are seeking a Senior Data Engineer with a deep understanding of healthcare data, including claims, clinical, and patient-reported outcomes data, and bioinformatics to design, build, and manage data pipelines for our health insurance clients. The ideal candidate is a lifelong learner with expertise in Python and a strong background in health, medical, and bioinformatics data, including a proven ability to interpret complex health-related datasets. Experience working with healthcare data standards (e.g., FHIR, HL7) is highly desirable. A subject matter expert (SME) in health and bioinformatics data is essential for success in this role. 

Responsibilities: 

• Design, build, and implement data systems that fuel our ML and AI models for our health insurance clients, ensuring compliance with healthcare data privacy and security regulations (e.g., HIPAA).
• Develop tools to extract and process diverse healthcare data sources, including electronic health records (EHRs), medical claims, pharmacy data, and genomic data, and create tools to profile and validate data.
• Work cross-functionally with data scientists to transform large amounts of health-related and bioinformatics data and store it in a format that facilitates modeling, paying close attention to data quality and integrity in the context of healthcare applications.
• Contribute to production operations, data pipelines, workflow management, reliability engineering, and more, with an understanding of the critical nature of data reliability in healthcare settings.
• Build the infrastructure required for optimal extraction, transformation, and loading of data from a variety of sources using SQL and AWS ‘big data’ technologies, including experience with healthcare-specific data warehousing and analytics platforms.
• Leverage expertise as a health and bioinformatics SME to ensure that data pipelines align with the specific requirements of health, medical, and bioinformatics data processing, including the ability to translate complex medical and biological concepts into data requirements. 

Qualifications: 

• BS in Computer Science, Bioinformatics, or another quantitative discipline; 5+ years of relevant working experience, with a significant portion focused on healthcare data.
• Proven experience working with and interpreting health, medical, and bioinformatics data is required, including experience with real-world healthcare datasets.
• Expertise as a subject matter expert (SME) in health and bioinformatics data, with a deep understanding of the nuances and challenges associated with processing medical and bioinformatics data, and a strong understanding of the healthcare industry.
• Experience working in Python and Airflow in a professional environment, ideally in a healthcare or life sciences setting. 

Key Changes and Rationale: 

• Emphasis on Specific Healthcare Data Types: Added examples like claims, clinical, patient-reported outcomes, EHRs, medical claims, pharmacy data, and genomic data to highlight the need for experience with diverse healthcare data.
• Healthcare Data Standards: Mentioned FHIR and HL7 to emphasize the importance of interoperability and standardized data exchange in healthcare.
• HIPAA and Data Privacy: Included a requirement for understanding healthcare data privacy and security regulations, particularly HIPAA, which is crucial in the industry.
• Data Quality and Integrity: Stressed the importance of data quality and integrity in the context of healthcare, as errors can have significant consequences.
• Healthcare-Specific Platforms: Mentioned experience with healthcare-specific data warehousing and analytics platforms as a bonus.
• Translation of Medical Concepts: Highlighted the ability to translate complex medical and biological concepts into data requirements, a critical skill for this role.
• Healthcare Industry Understanding: Emphasized the need for a strong understanding of the healthcare industry, not just technical skills.
• Real-World Healthcare Datasets: Specified experience with real-world healthcare datasets, indicating a need for practical experience.
• Healthcare Setting Experience: Preferred experience working in a healthcare or life sciences setting, showing a preference for candidates with direct industry exposure.