Staff Data Software Engineer
Opportunity Get Well is seeking an experienced and highly motivated Staff Data Software Engineer to help build and optimize our cloud-native data platform that powers AI, analytics, and clinical applications. You will play a key role in developing scalable, compliant data infrastructure and pipelines designed specifically for the healthcare domain, while mentoring junior engineers and leading cross-functional initiatives to drive innovation. This is a hands-on engineering role suitable for someone who thrives at the intersection of modern data engineering, cloud-native platforms, DevOps, healthcare data, and AI enablement. Candidates must have hands-on software development experience with at least one healthcare company—preferably in the provider space—with exposure to EHRs and core healthcare data domains. Responsibilities Data Engineering & Platform Development Design, build, and maintain scalable data pipelines supporting batch and real-time use cases. Develop and maintain production-grade data workflows that move and transform sensitive healthcare data across distributed systems at scale. Work with Spark, Databricks, Airflow/Temporal, and dbt to ingest, process, and manage structured and unstructured data. Design and implement reusable, efficient data models for analytics and AI/ML use cases. Ensure platform resiliency using CI/CD pipelines, observability tools, and logging frameworks. Leverage Infrastructure as Code practices using Terraform, CloudFormation, or equivalent to manage cloud resources. Healthcare Data Integration & Compliance Ingest and normalize complex healthcare data sets (FHIR, HL7, CCDA, Claims, EDI, Epic/Clarity, etc.). Familiarity with clinical coding systems and ontologies, such as ICD-10, SNOMED CT, LOINC, or RxNorm. Collaborate with compliance and security teams to ensure adherence to HIPAA, GDPR, and internal controls. Implement fine-grained access control, encryption at rest and in transit, audit logging, and data lineage strategies. AI & GenAI Enablement Work alongside AI/ML and data science teams to build pipelines feeding predictive and generative models. Tune data infrastructure for performance across distributed systems and hybrid data stores like SQL, MongoDB, ClickHouse. Prioritize scalability and flexibility in designing LLM-compatible pipelines and use GenAI best practices for healthcare use cases. Utilize Spark clusters and query frameworks such as SparkSQL and SPARQL for large-scale data access. Utilize and manage graph databases (e.g., Neo4j, Amazon Neptune, or similar) to support complex relationship modeling and healthcare data connectivity use cases. Enable high-quality training data pipelines and implement feature stores and model serving systems. Data Governance, Quality & Monitoring Implement data quality frameworks and establish robust validation processes using tools such as Great Expectations or equivalent. Build automated anomaly detection tools for data drift and integr...