Principal Member of Technical Staff

The Oracle Health & AI division pioneers innovation in healthcare through AI. Our mission is to revolutionize the healthcare industry by delivering modernized, automated, patient-centric care and ensuring the availability of cutting-edge clinical tools worldwide.

Within Oracle Health & AI, the Clinical Digital Assistant (CDA) team is at the forefront of this transformation. We are developing a next-generation platform for healthcare applications, harnessing advancements in generative AI technologies and OCI s cloud-native AI/ML and data infrastructure. This platform introduces capabilities such as conversational search, voice-assisted applications, summarization, semantic search, and RAG, redefining healthcare experiences for both patients and clinicians.

As a Senior Software Developer you will play a pivotal role in designing, implementing, and optimizing the data platform for CDA. You will work in an innovative, dynamic and collaborative team. If youre passionate about revolutionizing patient care and want to be at the forefront of healthcare technology, join us at Oracle Health & AI and make a meaningful difference in global healthcare.

Career Level - IC4

Basic Qualifications

• BS or MS degree in computer science, or equivalent
• 4-6 years of relevant software development experience

Strong Software Engineering Fundamentals & API Development

1. Proficiency in Data Structures & Algorithms: Critical for designing efficient systems that handle large-scale data.
2. System Design: Exposure to design of distributed systems and services.
3. Coding Skills: Expertise in languages like Java, Python, or Scala, especially for backend systems development.
4. RESTful Services: Proven experience in designing and building robust APIs for data access and integration.
5. GraphQL: Knowledge of modern data access technologies for flexible querying.
6. Microservices Architecture: Experience with creating microservices that handle different aspects of data management.

Data Architecture and Design Patterns

1. Database Design: Good knowledge of both relational (SQL) and non-relational (NoSQL) databases like Oracle, MongoDB, Cassandra, or DynamoDB.
2. Data Modeling: Understanding of data models for efficient storage and retrieval.
3. Data Warehousing: Experience with data warehouses and ETL processes.
4. Data Access Controls: Experience with role-based access control (RBAC) and encryption techniques to secure sensitive data.
5. Metadata Management: Familiarity with tools and processes for tracking data lineage, metadata catalogs (e.g., Apache Atlas, DataHub).
6. Ability to handle complex data-related challenges, from dealing with incomplete or inconsistent data to optimizing performance.
7. Strong analytical thinking to derive insights from data and build solutions that improve platform performance.

Data Pipeline and Orchestration

1. ETL/ELT Tools: Experience with data pipeline orchestration tools like Airflow, Prefect, or Dagster.
2. Automation & CI/CD: Familiarity with setting up CI/CD pipelines for data infrastructure.
3. Distributed Systems: Experience with technologies like Hadoop, Spark, Kafka, and Flink for managing large-scale data processing pipelines.
4. Data Streaming: Familiarity with real-time data processing frameworks (e.g., Apache Kafka, Pulsar, or RabbitMQ).
5. Cloud Platforms: Hands-on experience with cloud-based data services (AWS, GCP, Azure), including data storage (S3, GCS) and data analytics (EMR, Dataproc).

Leadership and Collaboration

1. Cross-Functional Collaboration: Experience working closely with other engineers to deliver on the platform s objectives.