Technical Product Marketing Manager – Oracle Autonomous AI Lakehouse

Oracle is seeking a seasoned Technical Product Marketing Manager to serve as the technical authority and competitive voice for Oracle Autonomous AI Lakehouse. In this highly visible, high-impact role, you will own technical positioning and differentiation across the full Autonomous AI Lakehouse portfolio—spanning converged database, vector search, AI pipelines, and open lakehouse capabilities built on Apache Iceberg and object storage. You will operate at the intersection of deep engineering knowledge and strategic marketing, translating architecture-level advantages into narratives that win technical evaluators, influence industry analysts, and displace the competition. 

We are looking for a technically fearless product marketer who thrives on rigor - someone who can reverse-engineer a competitor's benchmark, architect a convincing counter-narrative, and publish a TPC-style performance report, all while keeping a sharp eye on what customers and field teams actually need to close deals. You will partner directly with product management, engineering, and field engineering to ensure that Oracle's technical strengths are highlighted throughout our external communication. 

PREFERRED QUALIFICATIONS 

What You Bring: 

1. 8+ years of B2B technical product marketing, product management, or senior field engineering experience within data platforms, cloud databases, or AI/ML infrastructure—ideally at a hyperscaler, independent software vendor, or enterprise data company. 

1. Hands-on technical fluency with data lakehouse architectures, columnar storage formats (Parquet, ORC, Iceberg, Delta Lake), distributed query engines (Spark, Trino, Flink), and vector databases - sufficient to evaluate competitive products independently and identify substantive differentiation. 

1. Demonstrated experience designing, running, and publishing database or data warehouse performance benchmarks (TPC-DS, TPC-H, or custom workloads), including understanding of benchmark governance, reproducibility requirements, and FDR processes. 

1. Deep knowledge of the competitive data platform landscape - including Snowflake, Databricks, Google BigQuery, Azure Synapse/Fabric, and Amazon Redshift - and the ability to map their architectural decisions to customer trade-offs. 

1. Exceptional written and verbal communication skills, with a portfolio of published technical content - white papers, benchmark reports, architecture guides, or technical blogs - that demonstrate the ability to make complex ideas accessible without sacrificing accuracy. 

1. Prior experience supporting or leading analyst relations briefings and/or major vendor evaluations (Gartner MQ, Forrester Wave, IDC MarketScape) is strongly preferred. 

1. A self-starter mentality: comfortable owning complex, ambiguous projects with minimal supervision, managing competing priorities across engineering, marketing, and sales stakeholders simultaneously. 

1. Familiarity with Oracle Database, Oracle Exadata, OCI, or Oracle Autonomous Database is a plus but not required; demonstrated ability to develop deep product expertise rapidly is essential. 

What You'll Do: 

1. Own technical positioning end-to-end: develop and maintain the master technical narrative for Oracle Autonomous AI Lakehouse, including architecture comparisons, feature-by-feature competitive matrices, and TCO/ROI frameworks that clearly articulate Oracle's advantages over Snowflake, Databricks, Google BigQuery, AWS Redshift, and Microsoft Fabric. 

1. Lead competitive intelligence: conduct deep, hands-on technical analysis of competitive platforms—including evaluation of product documentation, release notes, conference sessions, and direct product trials—to produce authoritative competitive battle cards, technical briefs, and internal intelligence reports consumed by sales, field engineering, and executive leadership. 

1. Publish performance benchmarks: design, execute, and publish industry-credible performance benchmarks (TPC-DS, TPC-H, custom AI inference and RAG workloads) that demonstrate Oracle Autonomous AI Lakehouse's price-performance advantages; partner with engineering to ensure methodological integrity and defend results against third-party scrutiny. 

1. Create technical content assets: author white papers, solution briefs, deep-dive technical blogs, reference architectures, and hands-on labs that serve technical audiences includi