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Compunnel, Inc.

Sr SQL/Oracle DBA

Career Insights for Oracle Database Administrator

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What they do

An Oracle Database Administrator specializes in Oracle relational database architecture. Uses software to store and organize data, such as an organization's records or financial information. Ensures that users can easily access the information they need and that data are protected from unauthorized access.

$107,907 / year median in Georgia

-12% projected decline

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Job Description

If you are a passionate database engineer who would love to sit at the intersection of traditional enterprise database engineering and the emerging Gen AI revolution — building next-generation, AI-powered data solutions that transform how tens of thousands of users interact with the largest Human Capital Management system then we have a perfect role for you and we would like to have a discussion with you.
Education / Experience
/ Certification Bachelor's degree in computer science or related field (required) Master's degree in computer science, Data Science, or AI/ML (preferred) 10+ years of experience as a Database Developer/DBA in a fast-paced agile environment 7+ years as an Oracle Developer/DBA with expert-level PL/SQL and SQL skills 2+ years of hands-on experience with Generative AI, LLMs, or ML pipelines in a production environment (preferred) Deep expertise in database internals, expert-level PL/SQL, SQL, and Python/Shell programming Oracle Certified Professional (OCP) (huge plus) AWS Certified Machine Learning Specialty (plus) Essential Duties & Responsibilities Core Database Engineering Design and build scalable database services and solutions to complex business problems Debug critical database issues and provide root-cause analysis with long-term solutions Research, Design, Develop, and/or modify applications using SQL, PL/SQL, Python, and AI-assisted development tools Prototype solutions and recommend adoption of new technologies including Generative AI and LLM-powered database tooling Development and Deployment of application database changes/releases across production and non-production environments Build and maintain LLM-powered database assistants using frameworks such as LangChain, LlamaIndex, or similar Develop Retrieval-Augmented Generation (RAG) solutions using structured/unstructured data from Oracle and PostgreSQL databases Integrate AI-powered query optimization tools to enhance database performance tuning workflows Leverage GitHub Copilot, Amazon Q, or similar AI coding assistants to accelerate PL/SQL and Python development Build vector database integrations (pgvector, Oracle AI Vector Search) for semantic search capabilities Evaluate and adopt AI/ML model serving patterns (batch vs. real-time inference) for database-adjacent workloads Drive prompt engineering best practices for database-related AI applications