Investigate and resolve issues in revenue analytics, such as discrepancies in dashboards, KPIs, and pipeline metrics.
Audit and streamline data flows across various business systems (CRM, data warehouses, BI tools).
Create and manage lean, sustainable data models that support analytics and decision-making.
Collaborate closely with RevOps, Marketing, Sales, Customer Success, Product, and Finance teams to align and standardize data definitions and metrics.
Convert strategic business questions into structured analytical approaches.
Develop concise visuals and metrics that clearly explain complex data insights to executive stakeholders.
Proactively propose and implement robust solutions to systemic data and analytical issues, prioritizing long-term fixes over temporary patches.

Requirements

3–7 years in data-centric roles such as Revenue Operations, Data Analyst, Data Engineer, Analytics Engineer, Product Analytics, or similar positions.
Hands-on experience managing data from CRM and GTM systems (e.g., Salesforce, HubSpot).
Familiarity with core revenue operations tasks: pipeline tracking, forecasting models, funnel analytics, bookings reconciliation, and KPI definitions.
Strong proficiency in SQL; experience with data tools such as dbt, Looker, Snowflake, BigQuery, DuckDB, or Domo.
Lean Layer is tool agnostic, so we're more interested in people that bring relevant experience regardless of the tool, but most importantly, people that are passionate to learn new tools and concepts on the go.

Benefits

Offers Commission
Offers Bonus

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

SQLdata modelingdata analyticspipeline trackingforecasting modelsfunnel analyticsbookings reconciliationKPI definitionsdata visualizationanalytical problem solving

Soft Skills

collaborationcommunicationproblem solvingstrategic thinkingproactive approachattention to detailstakeholder managementadaptabilitycritical thinkingorganizational skills