Job Description
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Company Overview
An AI-native healthcare technology company is focused on improving healthcare outcomes by expanding access to precision medicine. Its platform helps biopharmaceutical manufacturers, payers, and health systems make clinical trials more accessible as a standard care option, improving patient access, advancing oncology outcomes, and reducing the total cost of care.
Key Responsibilities
- Own ingestion and transformation pipelines that bring patient records into the platform.
- Design and maintain reliable data workflows that transform raw healthcare data into trusted datasets for AI-assisted patient-trial matching and clinical workflows.
- Architect solutions for compute-intensive and long-running data processing workloads.
- Design asynchronous pipelines, message queues, and container-based compute solutions to support reliable processing at scale.
- Own data quality end-to-end, including schema design, validation, transformation logic, and monitoring.
- Build systems that identify data quality issues before they impact clinicians, users, or downstream workflows.
- Partner closely with AI engineers to establish data foundations for patient-trial matching systems.
- Collaborate with product engineers to determine how ingested data flows into critical user workflows.
- Monitor production systems and quickly triage and resolve operational issues.
- Make pragmatic technology decisions based on business requirements, scalability, reliability, and development velocity.
- Contribute to the continued evolution of the organization’s data architecture and engineering practices.
- Leverage AI coding tools to improve development, testing, measurement, and iteration.
- Help establish scalable and automated integrations that reduce reliance on manual data extraction processes.
Required Qualifications
- 8+ years of professional data engineering experience.
- At least 2 years operating at staff-level scope or demonstrating equivalent technical impact and ownership.
- Strong experience designing and operating data ingestion, transformation, and orchestration pipelines at meaningful scale.
- Deep SQL and PostgreSQL expertise, including schema design and query performance optimization.
- Strong Python skills and comfort working within TypeScript codebases.
- Deep experience with AWS, particularly Lambda, Fargate, SQS, and RDS.
- Ability to select and architect the appropriate AWS services for specific technical requirements.
- Strong understanding of data quality, reliability, validation, monitoring, and production operations.
- Proven experience using AI coding tools creatively and effectively to develop production systems.
- Demonstrated ability to independently identify and resolve systemic technical issues.
- Strong systems-thinking skills covering backend architecture, APIs, databases, scalability, and real-world production constraints.
- Excellent communication skills with the ability to explain technical trade-offs clearly to both technical and non-technical stakeholders.
- Strong ownership mentality and ability to operate effectively with ambiguity.
Engineering Culture
The organization values direct communication, strong ownership, low-ego collaboration, rapid decision-making, open feedback, and a strong bias toward action.
AI is deeply integrated into the organization’s product development and software development lifecycle. Engineering teams use AI tools for coding, testing, measurement, and iteration while maintaining strong engineering fundamentals and a focus on safe, production-ready systems.
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