Data Analyst, Revenue Operations

FacilityOS•North York, ON

2d•CA$85,000 - CA$95,000•Hybrid

About The Position

Revenue Operations at FacilityOS owns the data layer behind how we go to market and how we build that that layer out on Microsoft Fabric and Power BI, sourced primarily from Salesforce and our GTM stack. We are hiring a Data Analyst to own the models and reporting that sit on top of it. This is not a dashboard-maintenance role. You will build the semantic models, write the transformation logic, and be the person the GTM leaders and CRO come to when a number doesn't reconcile. The role suits someone who has spent two to four years doing real analytics work and wants ownership of a data platform rather than a queue of report requests.

Requirements

2–4 years in an analytics, BI, or data analyst role, ideally supporting a B2B SaaS revenue or GTM function
Strong SQL — joins, window functions, CTEs, query tuning
Hands-on Power BI: data modelling, DAX, star schema design, performance optimization
Experience building or maintaining data pipelines in a cloud platform. Microsoft Fabric is what we run; Synapse, Databricks, Snowflake, or BigQuery experience translates
Python for data work — transformation, API pulls, automation
Working knowledge of the Salesforce data model (objects, relationships, reporting quirks)
Able to take an ambiguous business question, decide what analysis actually answers it, and present the result to leaders without hand-holding

Nice To Haves

dbt or an equivalent transformation framework
Experience with Gainsight, Gong, SalesLoft, Clay, ZoomInfo, or HubSpot
Exposure to SaaS revenue metrics — ARR, NRR, pipeline coverage, CAC payback
Git and a habit of version-controlling analytical work

Responsibilities

Build and maintain Power BI semantic models and reports covering pipeline, bookings, forecast accuracy, conversion, retention, and customer health. Own the definitions, not just the visuals.
Build and maintain pipelines and notebooks bringing Salesforce and GTM system data into the lakehouse. Own transformation logic, refresh schedules, and data quality checks.
Perform quarterly pipeline and coverage analysis, segmentation and territory work, win/loss and cohort analysis, and ad hoc investigation into why a metric moved. Bring a recommendation, not just a chart.
Reconcile Salesforce against downstream systems, find and fix the breaks, and flag the process problems causing them.
Create documentation including metric definitions, model lineage, and runbooks.