About The Role The SaaS Tools and Innovation (TINNO) team is seeking a Senior Database Engineer (IC4) to design, build, and operate systems supporting SaaS license usage measurement, renewals insights, adoption tracking, and reporting. This role directly influences Oracle’s SaaS ecosystem by enabling actionable insights that drive renewals, customer success, and long-term product growth.
We are looking for an engineer with deep expertise in database and data engineering, combined with practical experience in Machine Learning (ML) and Generative AI, who can translate business and product requirements into scalable, production-ready solutions across multiple Oracle SaaS applications.
Qualifications
8+ years of professional experience in database engineering, data integration, and applied AI/ML.
Strong proficiency in Oracle Database (SQL, PL/SQL) with proven design and optimization skills.
Strong data engineering background, including ETL and streaming pipelines with multiple data sources.
Hands-on experience deploying ML models and Generative AI solutions into production systems.
Strong programming skills in Python or Java for integration, automation, and ML/AI workflows.
Experience with distributed systems and cloud-native platforms (OCI, AWS, Azure).
Ability to debug, optimize, and monitor complex cross-layer systems.
Strong communication and collaboration skills with functional awareness to ensure technical outcomes align with business needs.
Nice to Have
Experience building APIs or services to expose ML/AI insights to applications.
Familiarity with cloud-native AI/ML platforms (OCI AI/ML, AWS SageMaker, Azure ML).
Contributions to automation, observability, or operational efficiency in SaaS or enterprise-scale systems.
Experience integrating solutions across multiple Oracle SaaS or enterprise applications.
Database \& Data Engineering
Architect, design, and optimize relational databases that are secure, performant, and scalable.
Build and maintain PL/SQL packages, triggers, and complex queries supporting mission-critical workflows.
Develop robust ETL pipelines and streaming processes (GoldenGate, Kafka, Spark, Airflow, ODI) to integrate raw data across diverse Oracle sources.
Define strategies for data quality, governance, and reliability to support analytics and AI/ML models.
Machine Learning \& Generative AI
Develop and operationalize ML models for anomaly detection, predictive analytics, and intelligent classification.
Apply Generative AI to accelerate query optimization, improve observability, generate documentation, and increase developer productivity.
Performance, Reliability \& Observability
Lead performance tuning, query optimization, and capacity planning for complex workloads.
Implement proactive monitoring, dashboards, and alerting for both databases and AI/ML workflows.
Debug and resolve cross-system issues spanning databases, pipelines, and application layers.
Functional Awareness \& Application Collaboration
Translate product and business requirements into database and AI-driven technical designs that deliver measurable impact.
Collaborate with application developers and product teams to embed AI/ML insights into workflows and customer-facing solutions.
Ensure systems are aligned with functional objectives, adoption goals, and end-user needs.
Collaboration \& Ownership
Own end-to-end delivery of database, data engineering, and AI/ML solutions across multiple Oracle SaaS products.
Communicate complex technical concepts with clarity to both technical and non-technical stakeholders.
Drive innovation by adopting new tools, practices, and architectures that modernize the TINNO ecosystem.