Power user of conversational analytics, autonomous research agents, and natural-language SQL who turns warehouse-scale data into board-ready insight in minutes
An AI Data Analyst turns data into decisions with AI as a force multiplier: using LLM copilots for SQL and analysis, building self-serve semantic layers, and analyzing the new data AI systems generate (agent transcripts, eval scores, token costs). The craft has shifted from producing queries to producing judgment: framing questions, validating AI-drafted analyses, and telling decision-grade stories.
A distinctly 2026 mandate is analyzing AI itself: measuring automation quality and ROI, tracking adoption of AI tools, and giving leadership an honest read on what the AI portfolio returns. Analysts who own that scorecard sit unusually close to strategy for their level.
It's replacing query production while raising demand for analytical judgment: validating AI-drafted analyses, auditing automated decisions, and measuring AI ROI. Analysts who direct AI tools and own verification are gaining scope; report-runners are exposed.
Roughly $90k–$150k base in the US: consistently above traditional analyst bands, reflecting the premium attached to AI-fluent versions of existing roles.
Automation quality and eval-score tracking, AI ROI and token-cost analysis, adoption analytics for AI tools, and audits of AI-made decisions. Owning this scorecard puts analysts unusually close to executive decision-making.
Statistical verification (catching plausible-but-wrong AI output), semantic-layer/metrics design, AI-measurement literacy, and decision-grade communication. SQL stays necessary; it's no longer sufficient.