Headquarters:
Summary
We are seeking an Analytics Engineer / BI Lead for Channel Insights to define the semantic layer, build dashboards, and present verified insights to leadership. This role combines Tableau Next and Data 360 insight development, SQL/SOQL work, data-quality validation, field-user training, and daily use of AI copilots/agents to improve delivery speed while maintaining accountability for production decisions.
General information
This engagement is centered on Channel Insights, with responsibility spanning metrics, dimensions, refresh SLAs, dashboard logic, and executive-facing insight delivery. The work is not a report ticket queue; it requires ownership of the analytics layer and confidence in the numbers presented to leadership.
Daily use of AI copilots/agents is an explicit expectation for discovery, build, test, and documentation. The focus is on improving cycle time and cost-to-outcome versus legacy delivery, embedding automation into quality gates, and verifying outputs before they reach production or executive readouts.
Tasks and Deliverables
Own Channel Insights metrics, dimensions, and refresh SLAs with the CDP product owner and Data Expert.
Build Tableau Next and Data 360 calculated insights; write SQL/SOQL as needed.
Validate data quality with the modeler/engineer and fix breaks at the source.
Train field users through working sessions.
Use AI for SQL and dataset drafts and insight narratives; verify every number before executive readouts.
Automate anomaly commentary on Channel Insights where trustworthy; escalate false positives.
Required experience
5+ years of analytics engineering or BI lead experience.
Expert-level Tableau or equivalent BI platform experience.
Strong storytelling with data for executives and field leaders.
Experience using AI copilots/agents in analytics delivery with human verification.
Nice to have:
Experience with Salesforce CRM Analytics and/or Data 360.
Engagement highlights
Own a defined Channel Insights workstream across semantic-layer design, dashboards, and data quality.
Work directly with cross-functional stakeholders, including product, data, engineering, and field users.
Deliver executive-facing insights and automate repeatable commentary where the data is trustworthy.