About exacare ai exacare ai is a leading health tech company on a mission to build the AI operating system for post-acute care. Backed by Insight.
About the role
We’re looking for a Staff Data Engineer who thrives in an environment with strong ownership and can drive complex data initiatives from concept to production. You’ll build the pipelines, data models, and infrastructure that power our product, analytics, and AI workflows; turning complex healthcare data into reliable, usable foundations for our teams and customers. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Own data initiatives end-to-end: Take a data problem through technical design, implementation, validation, production rollout, and ongoing operation, without requiring constant senior oversight
- Build reliable data pipelines: Design and maintain scalable ingestion and transformation pipelines across application databases, APIs, third-party integrations, and healthcare data sources
- Bring systems-to-workflow thinking: Connect data architecture, models, and integrations to how customers, operators, and internal teams use the resulting information
- Design reusable data models: Create well-structured datasets and shared definitions that support product features, reporting, analytics, and machine learning
- Own data quality and production readiness: Build validation, monitoring, alerting, and recovery into pipelines so teams can trust the accuracy, freshness, and completeness of their data
- Scale performance and reliability: Improve processing efficiency, query performance, and infrastructure costs as data volume and product complexity grow
More about this role
We’re looking for a Staff Data Engineer who thrives in an environment with strong ownership and can drive complex data initiatives from concept to production. You’ll build the pipelines, data models, and infrastructure that power our product, analytics, and AI workflows; turning complex healthcare data into reliable, usable foundations for our teams and customers.
- Own data initiatives end-to-end: Take a data problem through technical design, implementation, validation, production rollout, and ongoing operation, without requiring constant senior oversight.
- Build reliable data pipelines: Design and maintain scalable ingestion and transformation pipelines across application databases, APIs, third-party integrations, and healthcare data sources.
- Bring systems-to-workflow thinking: Connect data architecture, models, and integrations to how customers, operators, and internal teams use the resulting information.
- Design reusable data models: Create well-structured datasets and shared definitions that support product features, reporting, analytics, and machine learning.
- Own data quality and production readiness: Build validation, monitoring, alerting, and recovery into pipelines...
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