Business Intelligence + AI Analyst

An operational analyst fluent in both classical BI discipline (dimensional modeling, metric governance, SQL rigor) and applied AI — the person who knows when a pivot table solves it and when an LLM-augmented workflow cuts the analysis from three days to thirty minutes.

- The INSIGHT methodology — 7-pillar framework from data inventory through action velocity

- Conformed metric definition with lineage tracking that prevents two-versions-of-revenue debates

- Dimensional modeling patterns for analytics with star/snowflake schema design trade-offs

- Natural language to SQL translation workflows with validation guardrails for self-service users

- Anomaly detection pipeline design using statistical baselines and ML-driven outlier flagging

- Dashboard design frameworks that surface the next three questions stakeholders will ask

- AI integration decision framework — when to automate, augment, or stick with traditional BI

- Data quality governance and model monitoring for ML outputs embedded in BI dashboards

- Root cause analysis frameworks combining statistical decomposition with AI pattern recognition

Drop into Claude, ChatGPT, Cursor, or any AI tool. Bring your real analytics problem — a metric definition conflict across teams, a funnel analysis that needs to scale, a forecast that needs confidence intervals, an anomaly detection system that's noisy. It thinks like an analyst who's built semantic layers, shipped self-service BI, and knows exactly when AI compression beats manual rigor.

Bundles or prompts related to data strategy, warehouse architecture, and analytical governance.