Lead Principal Platform Software Engineer

Oracle•Redwood City, CA

9d•$113,600 - $306,400

About The Position

Oracle’s AI Data Platform (AIDP) is a unified enterprise-grade cloud service bringing data management, governance, analytics, and generative AI development together. The Lead Principal Platform Software Engineer will lead initiatives that accelerate the development and adoption of data platform capabilities. The role involves designing, developing, and integrating advanced data engineering technologies for large-scale AI-enabled enterprises; providing technical leadership for data connectivity frameworks; and implementing capabilities such as zero-copy, Zero-ETL, and data ingestion. The engineer will architect and scale enterprise data lineage using extensible open standards, and build scalable, performant, secure frameworks to map end-to-end data flows, including column-level visibility from source ingestion through downstream analytics. The role collaborates with product, engineering, support, and operations stakeholders to influence the platform roadmap and deliver value to enterprise customers and partners. It requires working effectively in a large, geographically distributed team and delivering impactful results in a fast-paced environment.

Requirements

Demonstrated technical leadership and architectural experience setting direction for complex systems, resolving ambiguous requirements, and driving initiatives across multiple engineering teams.
Ability to balance scalability, reliability, security, performance, and delivery priorities.
Strong hands-on backend software engineering experience with a language such as Java or Python, including knowledge of API design, data modeling, and automated testing.
Ability to diagnose complex failures across service and infrastructure boundaries.
Strong understanding of concurrency, asynchronous processing, consistency, state management, retries, timeouts, idempotency, back-pressure, and recovery from partial failures, applied to large-scale or multi-tenant platform services.
Data and integration experience building pipelines, connectors, or data platform components that integrate with heterogeneous external systems, including management of schema evolution and API and data contracts.
Demonstrated hands-on use of Claude Code and/or Codex to deliver production software, including repository exploration, implementation planning, code generation, refactoring, debugging, test development, and code review.
Ability to independently assess AI-generated designs, code, and tests; identify incorrect assumptions and missing coverage; and validate correctness, security, performance, and maintainability.
Deep understanding of and accountability for delivered software.
Experience owning services from design through deployment and operations, including observability, service-level objectives, incident response, root-cause analysis, capacity planning, and durable corrective actions.
Ability to communicate trade-offs to engineering and product leaders and mentor senior engineers.

Nice To Haves

Experience with data platform technologies such as Apache Spark, SQL processing engines, notebooks, or lakehouse architectures.
Experience with orchestration or messaging platforms such as Apache Airflow, Temporal, Argo Workflows, Kafka, or comparable systems.
Familiarity with OCI or other major cloud platforms, Kubernetes, infrastructure as code, CI/CD, and cloud identity and authorization.
Experience designing extensible, connector-based architectures with API versioning and backward compatibility for platform services used by many teams.
Evidence of measurable engineering improvements through AI-assisted development, reusable agent workflows, and automated validation.

Responsibilities

Lead the design, development, and integration of data engineering technologies for large-scale AI-enabled enterprises.
Provide technical leadership for data connectivity frameworks and implement zero-copy, Zero-ETL, and data ingestion functionality.
Architect and scale enterprise data lineage functionality using extensible open standards.
Build scalable, performant, and secure frameworks that map end-to-end data flows and provide column-level visibility from source ingestion to downstream analytics.
Set technical direction for complex systems, resolve ambiguous requirements, and drive initiatives across multiple engineering teams.
Own services from design through deployment and operations, including observability, service-level objectives, incident response, root-cause analysis, capacity planning, and corrective actions.
Collaborate with product, engineering, support, and operations stakeholders to influence the AI Data Platform roadmap.
Communicate technical trade-offs to engineering and product leaders, mentor senior engineers, and build reusable tools and practices that improve quality and delivery.