At Zingage, we’re building AI-powered systems to automate the critical back-office operations of healthcare providers. Our goal this year is ambitious: scale from $X million to $XX million ARR by delivering intelligent staffing and scheduling agents to home care agencies nationwide.
We’ve found a powerful wedge: integrating deeply with Electronic Medical Records (EMRs). Today, we’re connected with over 300 healthcare sites, representing thousands of caregivers and patients. However, our growth has exposed significant foundational gaps in our data infrastructure, and we’re looking for exceptional engineers to help us solve them.
The Problem: Healthcare Data is Messy
Our competitive advantage lies in seamless EMR integrations—but EMR data is notoriously fragmented and unreliable:
- Multiple Integration Methods: Today, we integrate with EMRs through APIs, file dumps, and RPA. Each method brings a different set of challenges and can introduce issues like delayed responses, unpredictable changes, inconsistent schemas, and unreliable join keys.
- Scaling Pains (300 → 1,000 customers): Our current ETL architecture (Kubernetes pods → transforms → PostgreSQL) was fine initially. But now, write-intensive ETL tasks significantly impact our primary application database’s performance. Worse, performing transformations upfront (ETL) means losing raw data context, hindering debugging, and reducing flexibility.
- Limited Observability & Data Lineage: When data ingestion breaks (and it often does), our lack of lineage and replayability makes debugging slow and painful. Identifying root causes, replaying failed jobs, and rapidly restoring pipelines is currently difficult.
- Data Interpretability Challenges: Healthcare semantics are tricky—simple medical conditions can appear in many forms across EMRs. For example, the diagnosis “diabetes” might appear as five separate coded entries, though clinically identical. Building reliable AI means solving these interpretability challenges systematically.
- AI Data Readiness & Raw Data Storage: Our AI agent accesses data directly from our PostgreSQL database—but it’s restricted to already-transformed, limited-context data. Our AI needs access to richer historical and raw context data to perform optimally.
This combination of challenges—unreliable data, rigid ETL architecture, poor debugging capabilities, interpretability complexity, and insufficient AI-readiness—is limiting our scale, velocity, and product quality.
We must rethink our data architecture from first principles.
The Opportunity: A New Data Stack for Healthcare AI
We’re calling on talented backend and data engineers to help us architect and build a next-generation data pipeline capable of scaling to thousands of healthcare customers. This infrastructure will form the bedrock of our AI-powered staffing and scheduling solutions.
Here's some initial thoughts from our team:
1. Moves from ETL → ELT
- Extract & load raw data first (to a data lake like S3, BigQuery, or Snowflake).
- Delay transformations until downstream, enabling iterative experimentation, faster debugging, and improved raw data access.
2. Implements Event-Driven, Replayable Pipelines
- Capture EMR snapshot outputs as event streams (e.g., Kafka, Google Pub/Sub).
- Achieve full data lineage, observability, replayability, and rapid debugging—transforming our pipeline maintenance from reactive to proactive.
3. Adopts Modern Data Warehousing & Separation of Concerns
- Clearly separate transactional workloads (PostgreSQL) from analytical & AI workloads (BigQuery, Snowflake).
- Ensure high availability, query optimization, and real-time analytics without compromising app performance.
4. Scales Integration Operations
- Invest in automated integration infrastructure, robust error handling, and alerting.
- Scale to 1,000+ healthcare customers gracefully, without proportional increases in operational overhead.
5. Solves Data Interpretability with Systematic Normalization
- Develop modular semantic mapping frameworks or leverage healthcare data standards (FHIR, HL7, EVV).
- Tackle healthcare-specific data nuances systematically, ensuring AI models see consistent, high-quality data.
Why Join Zingage Now?
You’ll join a team of exceptional engineers (early Ramp, Amazon, Block/Square), backed by seasoned operators from healthcare and SaaS, all dedicated to rebuilding how healthcare is delivered through AI and automation.
If you’re energized by the idea of solving messy, mission-critical problems in healthcare—building a new foundational data architecture, owning impactful engineering decisions, and working on high-leverage problems at early-stage scale—we’d love to talk.