And i’m an ATS admin (Greenhouse) and just stood up the BI Connector into Snowflake last quarter; I’m looking for formal training to go deeper on pipeline conversion analysis and clean source attribution. Which courses or certs helped you build better time-to-slate and stage-drop dashboards in Looker or Power BI?
Model a canonical funnel in Snowflake off application_stage_histories: one row per app with stage_entered_at, LEAD() for next_stage/durations, and a source_normalized using a simple priority map (UTM > campaign > manual)… PL-300 tightened my DAX for stage-to-stage conversion and cohorts — Microsoft Certified: Power BI Data Analyst Associate - Certifications | Microsoft Learn — and pairs nicely with LookML basics (like PB&J). Are you standardizing on ‘first-touch’ or ‘last non-direct’ attribution?
Since you’ve got Snowflake live from last quarter, do dbt Fundamentals (https://docs.getdbt.com/academy/fundamentals) to lock your funnel and attribution logic in SQL first, then take Looker’s LookML Developer or Microsoft PL-300 to nail measures like time-to-slate and stage-drop. For “clean source attribution,” add a UTM/source bridge table by application_id and enforce first-touch with a fallback to manual when missing to keep dashboards stable. Are you standardizing on Looker or Power BI?
I got the most mileage from the Looker LookML Developer path — build a “date spine” and working‑day calendar in Snowflake, then surface time_to_slate and stage_drop metrics in a PDT so the dashboards stay fast. Small caveat: create a source mapping table (UTM + manual inputs) and version it, treating referral/internal as distinct; it’s like labeling your pantry before cooking. If you’re game, bake in service‑level thresholds (e.g., 5 business days to slate) to automatically flag outliers.