GTM Analytics Lead

Location: Remote US

Department: Finance

The Role

Reporting directly to the VP of Revenue Operations and serving as a key operational partner to the CRO, CCO, and CMO, the GTM Analytics Lead is a high impact role blending deep hands on technical execution with strategic executive influence.

In this role, you will own Apptegy’s GTM analytics and predictive modeling framework end-to-end. You will build and maintain predictive models (churn risk, propensity scoring, funnel velocity, and territory capacity) while maintaining the core reporting suite in Tableau. While starting as a highnautonomy technical builder, this position offers a clear runway to help build out and lead the GTM analytics team as Apptegy continues to scale.

What Youll Do

• Predictive Modeling & Advanced Analytics: Architect, build, and deploy predictive models using Python, SQL, and Snowflake to identify churn risks, score prospect propensity, project NRR expansion, and optimize GTM resource allocation.

• Executive Storytelling & Strategic Influence: Partner directly with the CFO, VP of RevOps, and GTM leadership to translate complex statistical analyses into clear, actionable narratives that guide quarterly strategy, Board presentations, and P&L reviews.

• Reporting & Dashboard Architecture: Design, modernize, and maintain Apptegy’s core reporting suite in Tableau, establishing a single source of truth for revenue metrics across Sales, Marketing, and Client Success.

• AI & Automation Workflow Integration: Champion the practical integration of AI/LLM workflows (Glean, Claude, Cursor) into daily analytics, automating data exploration, query generation, and operational anomaly detection.

• Data Infrastructure & Engineering Partnership: Partner closely with Data Engineering to define clean dbt metrics schemas, govern revenue definitions, and validate Salesforce and Snowflake data pipelines.

• Future Team Leadership & Foundation: Serve as the technical authority for GTM analytics today, establishing analytical rigor and laying the operational foundation to build and lead the analytics function as the team expands.

What Youll Bring

• 5–8+ years of progressive experience in GTM analytics, revenue operations, marketing analytics, or revenue data science within a B2B SaaS environment.

• Deep technical command of SQL, Python (pandas, scikit-learn, statsmodels), and cloud data warehouses (Snowflake or BigQuery).

• Demonstrated expertise in building production-grade predictive models (churn propensity, lead scoring, time-series forecasting, territory capacity).

• Advanced proficiency in constructing clear, executive-grade Tableau dashboards that business leaders rely on daily.

• Comprehensive understanding of B2B SaaS financial and operational metrics (ARR, NRR, GRR, CAC/LTV, win rates, cohort retention, and pipeline velocity).

• Exceptional communication skills—the ability to present statistical insights clearly to C-suite executives without hiding behind jargon.

• Goal-directed autonomy—the ability to take high-level strategic challenges, discover hidden revenue risks unprompted, and execute end-to-end solutions.

• Commitment to AI productivity—active integration of AI tools to accelerate code execution, data transformation, and reporting documentation.

What Makes You Stand Out

• Hands on experience developing custom MCP (Model Context Protocol) services, LLM driven chat analytics tools, or automated agentic workflows for enterprise data.

• Demonstrated track record of helping scale an analytics function from individual technical contributor to team lead.

• Master’s degree in Data Analytics, Computer Science, Economics, Statistics, or a related quantitative field (or equivalent practical experience).

• Experience with dbt data modeling and modern data stack orchestration (Airflow, Hex, dbt Cloud).

Compensation

The anticipated base salary range for this position is $120,000-$175,000, depending on qualifications, experience, location, and other job-related factors.

The final compensation offered may vary within this range based on the candidate’s skills, relevant experience, geographic location, internal equity, and the specific requirements of the role.