Data Analyst

micro1 is engaging Data Analysts to contribute expertise to a confidential client project focused on evaluating AI assistants in real-world analytical workflows leveraging cloud data warehouses. In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required — your domain knowledge is what matters.

1. Execute structured evaluation tasks simulating typical analytical workflows (e.g., anomaly investigation, KPI reporting, data refreshes) using AI-powered solutions with cloud data warehouse connectivity.
2. Independently verify AI-generated figures against source data by writing and running your own SQL queries, and assess the correctness of data joins, filters, and time windows.
3. Maintain, reset, and manage seeded datasets within Snowflake, ensuring data integrity and correct answer states for test scenarios.
4. Oversee warehouse roles, permissions, and access controls to facilitate secure and repeatable evaluation environments.
5. Configure and document connectivity and authentication processes across multiple analytics and SaaS platforms.
6. Investigate and document novel or undocumented product behavior encountered during workflow execution.
7. Participate in calibration sessions with peers to ensure consistency and rigor when grading or scoring outputs.

1. At least 3 years of hands-on experience as a data analyst or analytics engineer, with advanced SQL skills and expertise in Snowflake (warehouses, access control, query history).
2. Demonstrated proficiency in auditing and reconciling reported metrics against raw data, with a keen eye for catching subtle aggregation or logic errors.
3. Familiarity with business and finance analytics, including KPI definitions and reporting practices relevant to leadership or external stakeholders.
4. Experience in administering database access, managing roles, grants, and integrating authentication/security protocols; OAuth or security-integration familiarity is advantageous.
5. Working knowledge of common SaaS tools such as Slack, Google Workspace, or Microsoft 365 for sharing analytical outcomes.
6. Prior experience using AI assistants for analytics, with a critical perspective on the accuracy of AI-generated SQL and outputs.
7. Background in rubric-based evaluation, QA, or data labeling, with a meticulous approach and strong written and verbal communication skills.

Originally posted on Himalayas