Make messy data decision-ready. Profile, cleanse, reconcile, and document datasets so insights are reliable—even when the inputs are noisy.

Support new, “tip-of-the-spear” apps. Work with the application team to define the right data ingredients, stand up thin-slice pipelines, and validate results.

Move fast—carefully. Turn vague asks into testable hypotheses, build the analysis or dashboard, and ship quick iterations with attention to detail.

Guard data quality. Set up checks, audits, and alerts; track issues to closure and raise the bar on definitions, lineage, and reproducibility.

Automate the repetitive. Use SQL/Python (plus Airflow/Prefect/dbt or similar) to turn one-off analyses into lightweight, maintainable workflows.

Tell the story. Present findings with clear business context; highlight signal vs. noise, trade-offs, and recommended ...