Senior GTM operator · RevOps / MOPS / GTM Systems / AI GTM Engineering · 15+ years · London, UK · Available immediately
I build the operating infrastructure behind B2B revenue — the lifecycle rules, systems, data, reporting, governance and AI-assisted workflows that let Marketing, Sales and Revenue leadership operate from the same commercial truth.
My most recent role was Head of Global Marketing Operations at DevRev (Jan–Jun 2026). Before that I led global Marketing Operations, Revenue Systems and GTM transformation work across Contentsquare, Pleo, Nutanix, Equinix, FE fundinfo and Finastra.
| Scale / outcome | Evidence |
|---|---|
| 100 → 50 tools | Contentsquare martech rationalisation: –18% cost, +30% execution speed |
| +30% forecast accuracy | Unified Snowflake + Tableau revenue reporting layer at Contentsquare |
| –40% SDR response lag | AI-assisted routing / scoring work using Dust.ai and Qualified |
| +35% MQL → SQL | Pleo lead-to-deal operating model |
| +156% MQL | Nutanix multi-touch lead-engine transformation across 14 EMEA countries |
| $18M annual budget | Nutanix EMEA marketing scope |
| Global MOPS scale | Contentsquare COE supporting 120+ marketers with 4 direct reports |
| 2 direct + 8 matrix | Equinix global operating model with a $1.6M systems budget |
Full chronology and context: Professional Profile.
I am strongest where a role needs more than platform administration or reporting production.
- Head / Senior Director / VP Marketing Operations — operating model, martech, lifecycle, analytics, team design and governance
- Head / Director Revenue Operations — funnel definitions, pipeline governance, CRM operating model, attribution, forecasting and handoffs
- GTM Operations / Commercial Operations — cross-functional operating spine connecting Marketing, Sales, Product and Finance
- Revenue Systems / Marketing Technology — architecture, migrations, rationalisation, system authority and data quality
- AI GTM Operations / GTM Engineering / GTM Systems — signal and context design, decision routing, specialist automation, evidence contracts, human approval and version-controlled operating logic
A senior operator should be able to move across four levels without losing the thread:
- Commercial decision — what outcome or decision actually needs to improve?
- Operating model — who owns what, what are the definitions, handoffs and controls?
- Systems and data — how is that model represented in CRM, MAP, BI and integrations?
- Automation and AI — which parts can be accelerated safely, and where must a human remain accountable?
This portfolio shows all four: career outcomes, reconstructed operating case studies, reusable frameworks, AI/GTM architecture and runnable tools with tests.
Want something practical rather than another bio? Use the GTM Operating Diagnostic — a 105-point assessment across commercial definitions, lifecycle, systems, pipeline truth, Ops maturity, AI readiness and governance.
| You are reviewing me as a… | Recommended route | What it should help you judge |
|---|---|---|
| Recruiter / potential employer | Employer Brief → Professional Profile → Revenue Operating System case | Level, scope, leadership and commercial impact |
| CMO / Marketing leader | Campaign Operations → MOPS Operating Model → Pipeline Truth | Can I turn MOPS into commercially accountable infrastructure? |
| CRO / COO / Revenue leader | Operator Thesis → Revenue Operating System → Pipeline Truth | Can I create one operating spine across the revenue engine? |
| AI / GTM Engineering reviewer | AI GTM Operations → GTM Signal Normalizer → GTM Command Center → GTM Ops Router → Runnable Tools | Can I connect GTM signals, context and decision logic into something explicit, testable and safe to automate? |
| Investor / adviser | Investor Brief → Entrepreneur Journey | Can operator judgement become repeatable products, tools or services? |
My view of AI GTM Ops is not “add a chatbot to RevOps.” It is a controlled operating system where trusted signals and context, explicit decision logic, specialist automation and human accountability work together.
flowchart LR
A[CRM / MAP / Product signals] --> B[Signal contract]
B --> C[Trusted GTM context]
C --> D[Decision router]
D --> E[Specialist logic / skill]
E --> F[Evidence + recommendation]
F --> G{Human approval / evidence gate}
G -->|Approved| H[System action]
G -->|Insufficient evidence| C
H --> I[Outcome / learning record]
I --> C
- AI GTM Operations — my full operating model for signals, context, routing, evidence, control and learning
- GTM Signal Normalizer — synthetic CRM / MAP / product event contracts, provenance, deduplication and integration logic
- GTM Command Center — portfolio-safe orchestration architecture
- GTM Ops Decision Router — deterministic coordination logic with evidence gates and tests
- Pipeline Quality Scanner — explicit pipeline data-quality checks
- Lifecycle Transition Validator — lifecycle governance expressed as executable rules
The portfolio code is deliberately synthetic and transparent. It demonstrates how I translate GTM operating logic into something technical teams and AI systems can inspect — without claiming employer production code.
| Evidence | What you can inspect |
|---|---|
| GTM Operating Diagnostic | A practical 105-point diagnostic across commercial definitions, handoffs, systems, reporting, Ops maturity, AI readiness and governance |
| Revenue Operating System | Cross-functional diagnosis, operating design, sequencing and governance |
| Unified Lifecycle Governance | Definitions, qualification, routing, handoffs, recycling and adoption |
| Pipeline Truth & Attribution | Source, influence, progression, forecast evidence and reporting confidence |
| Campaign Operations OS | Intake, readiness, build, QA, launch, follow-up and improvement |
| Modern MOPS Team Operating Model | Team design, service model, priorities and distributed delivery |
| Operating Frameworks | Reusable operating models across revenue, lifecycle, pipeline and MOPS |
| Runnable Proof | Python demonstrations covering signal contracts, routing, pipeline quality and lifecycle controls with explicit rules, synthetic data and unit tests |
CRM / MAP / GTM systems: Salesforce · Marketo · HubSpot · Iterable · Chili Piper · LeanData · Segment · Hightouch · SalesLoft · Outreach
Data / analytics: Snowflake · Tableau · Google Analytics
AI / enrichment / automation: Dust.ai · Qualified · Clay · Clearbit · HG Insights
Current AI / build toolkit: Claude · Claude Code · ChatGPT · OpenAI Codex · GitHub · Python · Lovable · Notion
Portfolio-built technical proof: JSON contracts · event / webhook architecture patterns · deterministic routing · source provenance · idempotency · unit tests · CI
The tool list matters less than the operating principle: do not automate contested definitions, broken ownership or unreliable data.
- Contentsquare: 4 direct reports including one Director-level report; ~$1.8M MOPS / technology budget; Global MOPS COE supporting 120+ marketers across EMEA, AMER and APAC
- Pleo: 3 direct reports; ~$1.3M operations / technology budget
- Nutanix: 4 direct reports; $18M annual EMEA marketing budget across 14 countries
- Equinix: 2 direct + 8 matrix reports; $1.6M systems budget
Build the operating foundation first. Add automation and AI only when the underlying decisions, ownership and data can be trusted.
Commercial problem → ownership & process → systems → data & governance → analytics → automation & AI
I also apply the same operating discipline to independent products and ventures. These are not employers and are separate from my employment chronology.
This is a public professional portfolio, not an archive of employer material. Employer-related case studies are independently reconstructed, company-neutral and free from production data or employer-owned source artefacts. Synthetic, reconstructed and conceptual work is labelled explicitly.
See Confidentiality & Evidence Standard and Security.
LinkedIn: linkedin.com/in/christopherswarup
Public professional portfolio · Revenue systems, GTM operating models and AI-assisted operations that can survive contact with a real business.