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Build a governed people analytics layer on Rippling data in Looker — enabling HR, finance, and executive teams to explore headcount, attrition, and compensation metrics with consistent definitions.
The Rippling–Looker integration connects Rippling workforce data to Looker's governed analytics platform. HR, finance, and executive teams can explore headcount trends, attrition rates, compensation distributions, and organizational structure metrics in Looker Explore — with consistent metric definitions rather than individually-calculated spreadsheet numbers.
Looker's semantic layer ensures that when the People team reports headcount and when Finance reports headcount, they get the same number — because both are reading from the same Rippling-powered LookML model.
Teams that try to connect Rippling to Looker without first building a clean data model end up with dashboards that break when Rippling data changes. Rippling's employee data structure — with effective-dated records, multiple employment types, and multi-entity configurations — requires careful LookML design to produce reliable metrics.
thePeopleStack designs the Rippling-to-warehouse data pipeline and builds the LookML data model for workforce analytics. We define standard HR metrics — headcount, attrition rate, tenure, and compensation band distribution — as reusable Looker measures, and build initial dashboard templates for HR and finance teams.
For Canadian operations, thePeopleStack ensures Rippling's Canadian employee records are correctly represented in Looker data models and that any PIPEDA considerations for workforce data in Looker's cloud infrastructure are noted.
Rippling data flows into Looker through direct database connections or API-based data pipelines. thePeopleStack configures the data model (LookML) to represent Rippling's workforce entities — employees, departments, job levels, and employment events — in a format optimized for HR analytics exploration.
Looker's semantic layer (LookML) allows you to define reusable data models on top of Rippling data. Once the model is built, business users can explore headcount, attrition, compensation, and time-to-fill metrics without writing SQL — using Looker Explore or embedded dashboards.
Yes. Looker can join Rippling's workforce data with CRM data (pipeline per rep), finance data (headcount cost), and operational data to produce cross-functional dashboards that connect people metrics to business outcomes.
Looker connects to the underlying data warehouse (BigQuery, Snowflake, Redshift, etc.) rather than directly to Rippling. The Rippling data pipeline feeds into the warehouse, and Looker reads from there. thePeopleStack can advise on warehouse strategy if you're starting from scratch.
Initial LookML model build for Rippling workforce data typically takes two to four days, depending on the number of entities and metrics required. More complex multi-source models require additional scoping.