Descripción de la oferta
Fight churn with end‑to‑end GTM analytics and actionable insights. Spendesk is seeking a Full Stack Data (GTM / Retention) profile to help reduce churn by turning fragmented go‑to‑market signals into clear, trusted insights and concrete retention plays.
You’ll sit close to Revenue teams (RevOps, Sales, CSM, AM) and work hands‑on across the full analytics loop: framing problems, extracting and modeling data, building analyses and dashboards, and translating findings into recommendations that change how we operate.
This is not an ML‑heavy role. We’re looking for someone who thrives on qualitative + quantitative investigation, rigorous business analysis, and high‑impact storytelling. Your north star is simple: detect churn signals earlier, understand why they happen, and help teams act before it’s too late.
About The Team
You’ll be embedded with our Revenue Operations organization – the team accelerating growth by enabling Marketing, Sales, and Customer Success with data‑driven decision frameworks, operational excellence, and the right tooling. You’ll report to the Head of Data so that you have a long‑term stable reporting line beyond the churn reduction project and can access data assets (BI and tables) that provide foundational capabilities you’ll be building your churn score and business process optimizations upon.
You’ll partner closely with stakeholders on the ground (CSMs, AMs, Sales reps, Support, Product) and collaborate with our two data squads (“Datapoints” + “AI & Data Products”) to ensure definitions, datasets, and reporting foundations scale.
About the role
As a Full Stack Data (GTM / Retention), you will own the end‑to‑end analytics workstream around churn prevention. You’ll build and maintain a retention‑focused “source of truth” combining CRM, billing, product usage, support, and success interactions. You’ll run deep dives, design early warning indicators, and operationalize insights into dashboards and playbooks used weekly by GTM teams.
Over the first 6–12 months, success looks like: clearer churn definitions and drivers, reliable visibility from product usage to renewal outcomes, and a set of leading indicators and interventions that materially improve Gross Revenue Retention (GRR) and Net Revenue Retention (NRR).
Our tech environment
Data platform: Snowflake, dbt, BI tooling
Product analytics: Amplitude
Ingestion / exposure: Airbyte (cloud), Segment, Hightouch, Airflow
Collaboration & reliability: GitHub (versioning/CI), Synq (observability), Notion, Slack
GTM sources: HubSpot, Salesforce, Chargebee, Intercom
You don’t need to be a data engineer, but you should be comfortable working “full stack” in analytics: SQL + modeling + BI + stakeholder workflows.
Key Responsibilities
Define and maintain a robust measurement framework for churn and retention: logo churn vs revenue churn, GRR/NRR, contraction vs cancellation, renewal cohorts, and time windows.
Identify leading indicators of churn risk by combining product usage, engagement, support burden, onboarding progress, plan fit, payment behaviors, and relationship signals from CSM/AM notes.
Build and iterate on churn early‑warning dashboards and weekly monitoring routines with RevOps and CS leadership.
Perform root‑cause analyses on churn/contraction events and translate findings into prioritized, testable hypotheses.
Qualitative + Quantitative Investigations (Field‑Facing)
Run discovery with CSMs, AMs, Sales, and Support: capture what we suspect, then validate (or invalidate) with data.
Build structured churn narratives: what happened, when signals started, which segments are most affected, and what might have prevented the outcome.
Synthesize findings into clear recommendations (plays, process changes, product feedback loops) with measurable expected impact.
Data Modeling & Single Source of Truth for GTM Retention
Build and own analytics‑ready datasets in dbt that unify key GTM and product sources into consistent entities (accounts, contracts, renewals, users, usage, interactions).
Define and document metric logic and common dimensions (segments, cohorts, lifecycle stage, service level) to prevent “multiple truths” across teams.
Implement pragmatic quality checks and monitoring for retention‑critical datasets (freshness, completeness, key reconciliations).
Business Intelligence & Operationalization
Design and maintain dashboards for stakeholders across RevOps, CS, and Sales with drill‑downs by segment, geography, plan/service level, cohort, and lifecycle stage.
Create automated reporting and “decision support” views that help teams prioritize actions (watchlists, at‑risk cohorts, newly deteriorating accounts).
Support quarterly business reviews and forecasting discussions with retention insights grounded in data.
Cross‑Team Collaboration & Leverage
Partner with the Datapoints squad to align on the company semantic layer and ensure your GTM models fit the shared definitions.
Navigate with ambiguity: your recommendations to fight churn may involve changing observability of sales processes, CSM/AM daily routine, or stakeholder collaboration.
Act as a multiplier: help stakeholders become more data‑savvy and reduce ad‑hoc churn reporting by building durable assets.
What We’re Looking For
Experience & Background
4+ years of experience in analytics, RevOps analytics, data analysis, or analytics engineering in a B2B SaaS environment (Customer Success / retention exposure is a strong plus).
A track record of delivering insights that changed business decisions (not just building dashboards).
Experience working directly with GTM stakeholders (CSMs/AMs/Sales/RevOps) and navigating ambiguous problem spaces.
Technical & Data Skills
Strong SQL skills (CTEs, window functions, performance‑aware querying) and comfort exploring messy real‑world data.
Solid experience with dbt and/or data modeling practices (dimensional thinking, entity definitions, grains, cohorting).
Comfort with BI tools and building self‑serve dashboards that stakeholders actually use.
Familiarity with experimentation or quasi‑experimental thinking is a plus, but not mandatory.
Impact & Communication
Highly structured in thinking and able to turn complex analyses into simple, actionable recommendations.
Comfortable “in the field” with CSMs/AMs/Sales reps: you ask good questions, challenge assumptions, and build trust.
Outcome‑driven: you measure success by reduced churn, earlier detection, and better interventions—not by number of reports shipped.
Proactive and collaborative, with strong written and spoken English.
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