In this strategic individual contributor role, you will act as a "Hybrid Data Ecosystem Architect," driving the evolution of data environments that combine Oracle workloads with modern Lakehouse architectures.

You will define enterprise reference architectures for data engineering, specializing in semantic knowledge graphs, embeddings, and vector search at scale.

The ideal candidate is a technical visionary who can implement DevDataAISecOps principles to fuel advanced GenAI solutions while ensuring data provisioning remains secure, orderly, and resilient.

Responsibilities :

- Enterprise Reference Architecture: Define and lead the enterprise-wide reference architecture for data engineering frameworks, ensuring scalability across hybrid cloud environments.

- Hybrid Data Evolution: Lead the integration and migration of traditional Oracle workloads into modern Databricks Lakehouse ecosystems.

- GenAI & AI Data Preparation: Champion engineering strategies to prepare high-velocity data for semantic knowledge graphs, embeddings, and vector search, enabling enterprise-scale GenAI applications.

- Distributed Framework Leadership: Architect and oversee large-scale data processing using parallel/distributed frameworks such as Apache Spark, Flink, or Beam.

- Data Integration Strategy: Design and implement modern data integration patterns, shifting from traditional ETL to ELT, and managing Event Orchestration via API and Publish-Subscribe models.

- DevDataAISecOps Governance: Lead the adoption of DevOps, DataOps, and AIOps principles, utilizing GitHub/GitLab, Docker, and Kubernetes to implement Infrastructure as Code (IaC) and CI/CD.

- Semantic Modeling Pipelines: Build and optimize semantic modeling pipelines that support complex relational data definitions and advanced AI discovery.

- Security & Data Provisioning: Drive strategic decisions regarding data provisioning, ongoing security, and access controls to ensure enterprise data remains available and compliant.

- Automation & Roadmap Development: Design and lead the long-term roadmap for data solutions, automating reporting and business intelligence flows to reduce manual intervention.

- Technical Troubleshooting: Provide high-level leadership for ongoing database maintenance, performance tuning, and the resolution of complex troubleshooting issues within distributed systems.

Technical Requirements :

- Architecture Experience: 10+ years of progressive experience in database configuration, management, and architecture.

- Platform Mastery: 8+ years of deep expertise in Oracle Database Engineering and Databricks.

- Big Data Frameworks: 8+ years of hands-on experience with Apache Spark, Flink, or Beam.

- AI/ML Infrastructure: 8+ years of experience with vector search frameworks and semantic modeling.

- Modern DevOps: 2+ years of experience in DevDataAISecOps using Kubernetes, Docker, and CI/CD pipelines.

- Leadership Track Record: Minimum 5 years in a lead role driving strategic data-informed decisions.

Preferred Skills :

- Cloud Native Tools: Experience with Snowflake, BigQuery, or AWS Redshift.

- Advanced Orchestration: Familiarity with Airflow or Prefect for complex pipeline scheduling.

- Graph Databases: Experience with Neo4j or Amazon Neptune for knowledge graph implementation.

Core Competencies :

- Strategic Influence: Ability to lead enterprise-level data strategy efforts and influence senior management on AI investments.

- Problem Solving: Excellence in deconstructing complex data bottlenecks in distributed environments.

- Accountability: A proven ability to lead efforts that ensure data availability, security, and integrity at scale.

- Collaboration: Strong experience working in cross-functional environments to align data architecture with business solution design.