Enterprise MarTech & Systems Architecture · GTM & Revenue Systems · Data & Analytics Engineering · Applied AI

I architect and build governed systems across customer data, CRM, MarTech, revenue operations, analytics, and AI. My work connects commercial definitions to technical controls: canonical identity, field ownership, source provenance, durable state, reconciliation, and recovery when systems disagree or fail.

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Four selected systems demonstrate how I approach enterprise failures: define the problem, implement the controls, and make the resulting behavior inspectable.

1. Customer Signal & Activation

Problem: Fragmented customer identity, stale enrichment, unsafe sales handoffs and revenue outcomes that cannot be traced to collected cash.
Solution: Consent-aware signals, verified accounts, explainable fit and intent scoring, owner-safe CRM activation, AI-assisted seller review and fulfillment-to-cash reconciliation.
Evidence: Executed local workflows and a native Salesforce Developer Edition API integration tested with restricted synthetic records, permissions, approvals, idempotency and recovery. Inspect Salesforce execution evidence.

2. Enterprise MarTech / AI Control Plane

Problem: Connected platforms can overwrite authoritative data, repeat uncertain downstream actions or allow automation to exceed its permissions.
Solution: Python/PostgreSQL governance for canonical identity, source authority, consent, field ownership, durable execution, human approval and exception handling.
Evidence: Executed failure and recovery tests, including lost acknowledgments and receiver-level reconciliation; AWS deployment architecture is documented separately. Review recovery evidence · Architecture.

3. Revenue Metrics Platform

Problem: Pipeline, bookings, invoicing, cash and ARR are different economic measures, yet disconnected reports can treat them as interchangeable.
Solution: Governed SQL/dbt dimensional models, historical state, consistent metric definitions and source-traceable reconciliation bridges.
Evidence: Native Snowflake fixture acceptance passed across three builds, each with 15 models and 30 tests. The larger synthetic enterprise scenario was verified locally, not cloud-benchmarked. Inspect Snowflake execution evidence.

4. First-Party Measurement Assurance

Problem: Browser and offline conversion reporting can miss events or overstate revenue by confusing proposals, signed contracts, deposits and payments.
Solution: Consent-aware browser events, GTM-compatible data layers, source-authorized offline conversions, durable receipts and SQL/dbt reconciliation.
Evidence: Executed browser and local warehouse workflows; native BigQuery Studio queries against Google's public GA4 sample exposed 14 purchase events versus 57 nested item rows and preserved two revenue discrepancies. This Google sample evidence is separate from the synthetic business scenario. BigQuery execution evidence · Passing Windows/Linux CI.

Alongside the four implementations above, these complete ten core engineering projects. Each addresses a different enterprise failure or decision.

• MarTech Data Reliability — Problem: Late, duplicate or corrected data can invalidate marketing reports. System: Contract-checked ingestion, quarantines, corrections and reconciliation. Benefit: A bad load cannot silently replace the last validated publication.
• MarTech Migration Assurance — Problem: Moving platforms can preserve row counts while corrupting business meaning. System: Semantic parity tests, change catch-up, cutover gates and evidence-bound rollback. Benefit: Unsafe migrations are stopped before customers' records are misrepresented.
• Temporal Customer Audiences — Problem: Customer eligibility changes, and late-arriving facts may correct what was known yesterday. System: Business-time and knowledge-time history, restatements, expiry and activation reconciliation. Benefit: Audiences reflect current eligibility while earlier decisions remain auditable.
• Enterprise Agent Runtime & Evaluation — Problem: AI output can sound reasonable while being unsafe or unsupported. System: Actual local model inference, adversarial tests, source checks and release gates. Benefit: Unsafe agent versions can be blocked instead of promoted on persuasive prose alone.
• MarTech Estate Impact — Problem: Retiring a seemingly redundant platform can break hidden business workflows. System: Evidence-backed dependency maps and change-impact analysis that preserve uncertainty. Benefit: Consolidation decisions have inspectable justification.
• MarTech Stack Economics — Problem: The cheapest integration design can miss deadlines or API limits during peak demand. System: Constrained, independently checked optimization across cost, capacity, quotas and freshness. Benefit: Proposed savings are evaluated against service requirements.

An at-a-glance view of the ten implementations and the enterprise problems they address:

View the full-size diagram.

Enterprise Revenue Systems Lab — browser-based evidence explorer · Source code and reproduction

For a closer look at selected implementation behaviors, the Lab offers three independent investigations:

• AI seller decisions: Examine a model-generated recommendation, its supporting sources, a reviewed edit, approval and recorded delivery evidence.
• Execution recovery: Trace a lost acknowledgment and worker interruption through durable workflow state and independent receiver receipts, including duplicate prevention.
• Revenue reconciliation: Explore why installed ARR, bookings, invoices and collected cash differ, with a source-backed financial bridge.

No installation is needed to explore. The hosted site presents recorded, verifiable synthetic evidence; fresh inference and original-engine execution are available only through separate local workflows.

Enterprise platforms and business systems: Salesforce · HubSpot · Salesforce Marketing Cloud · Marketing Cloud Account Engagement (Pardot) · Marketo · Segment · Clay · LinkedIn Sales Navigator · HighLevel (GoHighLevel) · LMN · Aspire · QuickBooks · Google Analytics 4 (GA4) · Google Tag Manager (GTM) · Adobe Analytics · Tableau · Power BI · Vapi · Dialpad · Make · n8n · Workato · Zapier

Engineering, data platforms, and cloud: Python · SQL · JavaScript / TypeScript · PostgreSQL · Snowflake · Supabase · dbt · DuckDB · SQLite · FastAPI · Streamlit · Airflow · Git · GitHub Actions / CI/CD · Docker · Terraform · AWS architecture (ECS/Fargate, RDS, ECR, IAM/OIDC)

Data architecture and enterprise integration: Dimensional modeling · SCD Type 2 · Bitemporal data · Governed metrics · Data contracts · Schema evolution · ETL / ELT · Reverse ETL / warehouse-to-CRM activation · Incremental processing · Backfills · REST APIs · Webhooks · OAuth 2.0 · Canonical identity · Entity resolution · Source lineage · GTM engineering · Lead scoring and routing · SDR/AE workflow automation · Sales engagement systems · Pipeline attribution · Meta Conversions API · Google Enhanced Conversions · Offline conversion tracking · Server-side measurement

Reliability, governance, and AI systems: Source authority · Field ownership · No-Regress Logic · Stateful middleware · Idempotency · Replay safety · Retry/backoff · Exception queues · RBAC · Consent governance · Audit logging · Revenue reconciliation · Failure recovery · Release gates · OpenAI / LLM APIs · Anthropic Claude · Qwen local inference · llama.cpp · Model Context Protocol (MCP) · Prompt engineering · Tool/function calling · Structured outputs · LLM evaluation · Human approval controls

Each repository provides implementation details, tests, execution evidence, and known limitations. The public systems use synthetic or vendor-neutral data so the architecture can be inspected without exposing private client environments. They demonstrate engineering patterns, not a claim that any public repository was deployed unchanged into client production.

Tools execute. The control layer determines what is true.