Database Administrator

Job Summary

The Database Administrator analyzes, optimizes, evaluates, and structures complex relational environments and data schemas to train frontier AI models and autonomous agents. Core daily duties include engineering realistic data workloads; debugging and fine-tuning SQL scripts; constructing backup and disaster recovery variations; documenting structural best practices; and establishing rigid quality assurance grading rubrics to test model troubleshooting, query efficiency, and performance optimization logic.

Foundational Capabilities & Prerequisites

• Relational Core Depth: Advanced hands-on expertise with MySQL and PostgreSQL administration.
• Performance Engineering: Proven track record in detailed database optimization, query tuning, and environment troubleshooting.
• Scripting & Command: Strong command of SQL for writing complex queries, scripting automation, and diagnosing performance bottlenecks.
• System Safeguards: Direct experience planning, configuring, and testing backup, restore, and disaster recovery processes.
• Cognitive Matrix: Proactive problem-solving approach combined with strong analytical thinking and attention to detail.
• Articulation Mechanics: Excellent written and verbal communication skills, showing a commitment to clearly documenting and explaining complex technical information.
• Distributed Alignment: Ability to work effectively within a remote, distributed, and collaborative team environment.

Preferred Technical Multipliers

• Security & Governance: Operational experience implementing and managing database security best practices.
• Cloud & Automation: Familiarity with cloud-based database solutions (e.g., AWS RDS, Cloud SQL) and infrastructure automation tools.
• Cross-Functional Synergy: Past experience collaborating with multi-disciplinary or global teams in an asynchronous setting.
• AI Evaluation Exposure: Past experience training AI models, validating large language model outputs, or designing technical simulation datasets.

Technical Workflow Architecture

As a Database Administrator at micro1, your daily contributions will balance database engineering with machine learning data design:

• Synthetic Scenario Prototyping: Designing extensive, complex relational database schemas featuring intentional anti-patterns—such as missing indexes, unnormalized tables, or deadlocking transactions—to evaluate whether AI agents can safely diagnose and repair system bottlenecks.
• Adversarial Query Engineering: Writing highly specific technical instructions, unoptimized SQL statements, and prompt matrices that mimic broken enterprise environments to see if the AI model can accurately refactor code and protect data integrity.
• Disaster Recovery Validation: Structuring faulty backup scripts or broken recovery configurations to test if an AI engine can accurately spot execution errors, rebuild transaction logs, and implement strict recovery parameters.
• Performance Failure Diagnostics: Auditing automated log outputs, query execution plans, and terminal commands generated by AI database tools, isolating structural gaps in their logic, and updating data templates to fix those engineering blind spots.

Key Responsibilities

1. Database Optimization & Dataset Synthesis

• Analyze and optimize database performance profiles within large-scale MySQL and PostgreSQL environments to act as training baselines.
• Monitor database configurations proactively to identify performance bottlenecks, using these metrics to engineer diverse, real-world data training scenarios.
• Develop, test, and manage realistic data backup and disaster recovery procedures to safeguard simulated environments and evaluate model competency.

2. Collaborative Schema Architecture & Security

• Partner with cross-functional development teams to design, implement, and maintain robust database structures aligned with evolving project needs.
• Implement and manage database security best practices, compliance protocols, and permission matrices across training datasets.
• Stay current with emerging SQL features and relational database technologies to drive continuous improvement within the data lab platform.

3. Rigorous Evaluation, QA & Rubric Construction

• Create comprehensive documentation and structured evaluation rubrics to conduct rigid quality assurance reviews on AI-generated database code and architecture schemas.
• Audit model performance outputs, identify repeating patterns in algorithmic reasoning errors, and refine data inputs to maximize model stability.
• Communicate proactively with team members, detailing progress, dataset breakthroughs, and technical roadblocks cleanly.

Core Competencies & Analytical Strengths

• SQL Logic Verification: Applying deep query execution knowledge to spot index failures, syntax vulnerabilities, and transaction management weaknesses in model code outputs.
• Failure Scenario Simulation: Engineering realistic database engine errors, lock contentions, and resource exhaustion prompts to expose AI reasoning gaps.
• Asynchronous Data Scale: Organizing and generating clean, highly formatted database schema templates that data science teams can immediately feed into machine learning loops.

Skills Required:

• Computer / Software / It / Data