We are looking for a Mid-Level Data Engineer to help design, develop, test, and support cloud-based healthcare data pipelines and backend services on Azure/AWS/Google Cloud Platform. This role will work closely with senior engineers to process healthcare files from GCS, support event-driven workflows using Pub/Sub/Eventarc, implement data processing using Cloud Run/Dataflow, load data into SQL/BigQuery, relational databases, document databases, and support integration with FHIR Store and downstream applications.

The role may also support search and AI-assisted capabilities for healthcare PDFs, CCDA files, FHIR data, and clinical documents using search indexes, embeddings, NER tools, and semantic search pipelines.

Required Skills :

- 5 - 9 years of hands-on database experience, data engineering experience including relational databases, Bigquery and document databases

- 3+ years of hands-on database experience, including SQL, relational database concepts, data modeling, query writing, indexing basics, and data validation.

- 2+ years of cloud experience, preferably on Google Cloud Platform.

- Strong programming experience, especially with Python, including :

1. Object-oriented programming

2. Modular code development

3. Error handling

4. Logging

5. Unit testing

6. Package management

7. Backend or data pipeline development

- Experience with RDBMS technologies, such as PostgreSQL, MySQL, SQL Server, Oracle, or equivalent.

- Experience building or supporting data pipelines, including batch, event-driven, API based, or streaming workflows.

- Hands-on experience with GCP services such as: GCS, Pub/sub, Cloud Run, Data flow

- Experience integrating with external APIs, including authentication, retries, error handling, and basic monitoring.

- Docker experience for containerized application development.

- Git/GitHub experience, including branching, pull requests, and code reviews.

- Experience with CI/CD pipelines, preferably using GitHub Actions, Cloud Build, or similar tools.

- Ability to debug application, pipeline, database, and cloud service issues using logs and monitoring tools.

- Ability to write clean, maintainable, testable, and well-documented code.

- Ability to work from architecture diagrams, technical specifications, and implementation tickets.

Preferred Skills :

- Cloud certification or Database Certification

- Google Cloud Healthcare API experience.

- Exposure to FHIR R4, HL7, CCDA, clinical data integration, or healthcare interoperability.

- Exposure to embeddings, semantic search, ElasticSearch, OpenSearch, or vector databases.

- Experience with Gemini, Vertex AI, OpenAI API, or other LLM API integrations.

- Terraform or Infrastructure-as-Code exposure.

- HIPAA, PHI, healthcare security, or compliance awareness.

Key Responsibilities :

- Design, build and support scalable healthcare data pipelines on GCP.

- Process healthcare files from GCS and route them through Pub/Sub, Eventarc, Cloud Run, Dataflow, BigQuery, FHIR Store, and downstream systems.

- Design relational and cloud database schemas for operational, analytical, and document oriented workloads.

- Build APIs to monitor pipeline status, file processing, Pub/Sub messages, Dataflow jobs, Cloud Run services, BigQuery loads, and FHIR data validation.

- Design database models for pipeline metadata, orchestration configuration, processing history, audit logs, document metadata, and search indexes.

- Implement batch and streaming data pipelines.

- Build data validation, reconciliation, retry, and dead-letter handling processes.

- Integrate structured, semi-structured, and unstructured healthcare data.

- Support semantic search, NER, embeddings, and search result snippets for healthcare documents.

- Optimize database performance, query cost, indexing strategy, and storage design.

- Implement monitoring, logging, alerting, and operational traceability.

- Ensure secure handling of healthcare data, including PHI-aware design and least privilege access.

- Mentor mid-level and junior engineers and define engineering best practices.

- Debug production and non-production issues using Cloud Logging, Cloud Monitoring, and application logs.

- Participate in code reviews and follow engineering best practices.

- Work with senior engineers to implement scalable, secure, and maintainable cloud data solutions.