Revenue Systems Engineer · GTM Systems & RevOps

Turning fragmented GTM signals into controlled action.

I design and build end-to-end revenue infrastructure connecting CRM, automation, product signals, operational data, qualification, routing, and AI-assisted intelligence.

Open to remote opportunities and relocation.

CONTROL PLANE
GTM signalsProduct · Lead · Account
Decision layerIdentity · Rules · State
Operational actionCRM · Routing · Handoff
Auditable by design
3End-to-end systems
2CRM ecosystems
1Operational design discipline
EvidenceArchitecture · Tests · Failure paths

Selected implementations

Revenue systems built around real operating problems.

Each project is independently designed and implemented using synthetic or public-safe data, with explicit architecture, edge cases, controls, evidence, and handoff documentation.

P1HubSpot-centered

Product-Led Revenue Qualification & Sales Handoff

Converts product and signup events into validated, identity-resolved, qualified, and auditable sales handoffs.

  • Identity resolution and CRM context
  • Deterministic qualification and routing
  • Duplicate-event and active-deal guards
  • Persistent state and failure handling
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P2Salesforce-centered

Lead-to-Opportunity Revenue Operations System

Controls the lead lifecycle from intake through qualification, routing, SLA enforcement, CRM handoff, and recovery.

  • Inbound validation and normalization
  • Qualification and ownership routing
  • SLA-controlled sales follow-up
  • API failure and retry handling
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P3AI-assisted

GTM Intelligence & Account Prioritization

Transforms account data and GTM signals into enriched, evidence-aware, prioritized, and reviewable decisions.

  • Firmographic and signal enrichment
  • Structured Claygent research
  • Explainable deterministic scoring
  • Human review for uncertainty
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Capability map

Across the revenue infrastructure lifecycle.

01

CRM architecture

Lifecycle fields, object relationships, ownership, associations, and operational source-of-truth boundaries.

02

Automation

Event-driven orchestration, webhooks, API operations, branching, retries, and controlled handoffs.

03

Data controls

Validation, normalization, identity resolution, idempotency, persistent state, and auditability.

04

Revenue operations

Qualification, routing, SLA enforcement, exception handling, and sales-ready operational outputs.

05

AI-assisted intelligence

Structured research, evidence preservation, explainable scoring, confidence controls, and human review.

06

Delivery discipline

Requirements, architecture decisions, scenario testing, failure-path evidence, documentation, and handoff.

Engineering principles

Automation is only useful when the operating logic is trustworthy.

The portfolio emphasizes the controls that make revenue automation maintainable: clear ownership, durable state, deterministic business rules, visible failure paths, and evidence that the system behaves as designed.

01
Source-of-truth clarity

Each system assigns the right responsibility to CRM, orchestration, and operational data layers.

02
Deterministic control

Critical qualification and routing outcomes remain explainable and testable.

03
Failure-aware design

Duplicate events, missing identities, API errors, and uncertain AI outputs have explicit paths.

04
Evidence before claims

Capabilities are supported by architecture, configuration, test scenarios, and implementation artifacts.

Contact

Let’s discuss the revenue system behind the workflow.

Available for relevant remote opportunities, project-based work, and relocation conversations.