Sr SQL/Oracle DBA

Compunnel, Inc.; Alpharetta, Northern (GA, KY); Hybrid; USD 140,000 - 180,000; Full time

Job summary

Compunnel, Inc. seeks a senior database engineer to bridge traditional enterprise DB engineering with Gen AI-powered data solutions. You will optimize Oracle/PostgreSQL data services, build AI-assisted tooling, and enable large language models for database workloads.

The role emphasizes PL/SQL mastery, Python scripting, and hands-on experience with AI pipelines and RAG techniques in production environments.

Qualifications

• Bachelors in CS or related field is required.
• Master’s in CS/Data Science/AI/ML is preferred.
• 10+ years as a Database Developer/DBA in agile settings.
• 7+ years Oracle Development/DBA with expert PL/SQL.
• 2+ years Generative AI/ML in production environments preferred.
• Deep expertise in databases, PL/SQL, SQL, Python/Shell.
• AWS Certified Machine Learning Specialty is a plus.

Responsibilities

• Design and build scalable database services for complex business problems.
• Debug critical DB issues and provide long-term root-cause solutions.
• Develop using SQL, PL/SQL, Python, and AI-assisted tools.
• Prototype AI-powered DB tooling and evaluate new tech.
• Manage app DB changes across production and non-prod environments.
• Build and maintain LLM-powered database assistants (LangChain/LlamaIndex).
• Develop RAG solutions using Oracle and PostgreSQL data.
• Integrate AI-powered query optimization into tuning workflows.
• Leverage AI coding assistants to accelerate PL/SQL and Python development.
• Create vector DB integrations for semantic search (pgvector, Oracle AI Vector Search).
• Assess AI/ML model serving patterns for DB-adjacent workloads.
• Promote prompt engineering best practices for DB AI apps.

Skills

PL/SQL

SQL

Python

Oracle DBA

Generative AI

Data pipelines

AI tooling

LangChain

Education

Bachelor's degree in CS

Master's degree in CS/AI/ML (preferred)

Tools

LangChain

LlamaIndex

GitHub Copilot

Amazon Q

pgvector

Oracle AI Vector Search

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
• AWS Certified Machine Learning Specialty (plus)

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