Revenue Operations GTM Engineering Workflow Automation Sales Strategy

I build the revenue systems teams run on — the part where GTM strategy becomes execution.

Pipeline logic, lead and deal scoring, and the automation underneath. Built to be operated by the team, not admired in a slide.

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01

Custom GTM Tooling

extraction · self-healing

Self-Writing Web Scraper

The output schema is the contract; an LLM writes the per-site extraction logic at runtime and repairs it when a site changes. A structural fingerprint keeps the model — and the cost — out of the steady state.

Python · Playwright · Claude API · Pydantic

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Self-writing extraction pipeline schema is the contract → the LLM only fires on first contact or drift

Self-writing extraction pipeline, left to right: a URL and a target schema go in. The scraper fetches the page and snapshots the raw HTML after a robots.txt check; JS-heavy pages fall back to a headless render. The DOM is compressed to a signal-rich view of JSON-LD and repeating-row exemplars, then hashed into a structural fingerprint and looked up in a spec cache. On a cache hit the known-good spec is reused and the LLM is skipped at zero cost; on a miss or drift, Claude writes an ExtractionSpec via structured outputs, reading the fixed JobPosting schema — the contract — at runtime. The generated spec is validated against the schema with Pydantic row and null-rate floors; validation errors are fed back to the model in a single-model self-heal loop. A valid spec, or a reused cached one, is interpreted deterministically with css, xpath, and regex selectors, producing schema-valid JSON and a reproducible run record.

analytics · governed metrics

GTM Funnel Analytics

Four dashboards showed four different 'pipeline created' numbers. A dbt layer reconciles them to one canonical figure — and a test fails the build if any gap goes unexplained.

dbt · DuckDB · SQL · Evidence.dev

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Reconciling “pipeline created” five sources → one canonical number

Reconciliation of the "Pipeline Created" metric across five sources, shown as horizontal bars against a canonical reference line at $6.88M. Sales (Salesforce) reports $13.4M, 95% high, applying no exclusions — renewals, partner deals, $0 and duplicate opportunities are all counted. Finance reports $8.3M, 20% high, bucketing by close date and including renewals. The canonical value from this repo is $6.88M — net-new opportunities at Stage 1 or higher with exclusions applied (KPI #5). The data team (Looker) reports $6.3M, 8% low, from inner-joining a broken id crosswalk. Marketing (HubSpot) reports $4.5M, 35% low, counting marketing-sourced only at an MQL threshold of 40 instead of 50. A dbt test fails the build if any gap is left unexplained.

pipeline · B2B SaaS

Deal Risk Scoring

B2B pipeline risk scoring. Cheap heuristics rank every deal; Claude explains only the ones worth a second look.

Python · Streamlit · pandas · Claude API

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Two-layer risk scoring cheap heuristics rank every deal → Claude explains only the top 10

Deal Triage two-layer scoring flow, left to right: pipeline deals come in from HubSpot, a CSV, or sample data and are normalized to open deals only, with closed or unmapped deals filtered out of scope. Layer one is a zero-cost deterministic heuristic that scores three signals against the team's own per-stage medians into a composite 0 to 100 risk score, sorting deals into High, Medium, or Low risk. Layer two ranks them and sends only the top 10 at-risk deals to Claude, which reads the editable RevOps-owned deal-risk prompt at runtime and returns output tiered by signal strength: a full memo with verbatim quotes when the signal is strong, or a short brief with no fabrication when it is thin. Output feeds a follow-up email draft and feedback capture.

Other projects

02

Stack

The lightest tool that keeps the logic legible.

CRM & GTM Platforms
SalesforceHubSpotAttioOutreachGongApolloClay

The systems of record and the tools that feed them — Salesforce or HubSpot at the core, with sourcing, sequencing, and conversation data wired in.

Modern Data Stack
SQLdbtSnowflakeFivetranHightouchLooker

Pipeline history modeled where it can be trusted — ELT in, dbt in the middle, reverse-ETL back out to the tools the team actually uses.

Automation & AI
Claude CodeMCPn8nZapierPythonNext.jsREST APIs

The glue and the judgment layer — agentic AI and orchestration that take friction out of the stack and keep the logic legible.

03

Focus Areas

01

RevOps systems design

Pipeline definitions, stage logic, scoring, and forecasting — built so a team can operate them, not just admire them in a slide.

02

GTM tooling & automation

Scoping and building the internal tools and workflows that move the number: lead and deal triage, Slack and HubSpot automation.

03

Analytics & instrumentation

The SQL and dbt layer underneath it all — decisions tuned against real outcomes instead of gut feel.

04

About

Julian Ross

I spent more than a decade in sales, first closing business as a full-cycle rep and then building and leading a sales development team. A lot of that work went to wrestling with the mis-configured GTM tools and broken processes. Leads got routed to the wrong rep or fell out of the pipeline entirely, automations silently broke or just added overhead, dashboards went ignored because the data was unreliable, and buying signals or risk flags failed to reach anyone before a deal was lost or a customer churned. Watching processes and tooling fail this way is what led me to Revenue Operations and GTM Engineering.

I've worked across the spectrum of tech companies, from Series A startups with almost no systems in place to public companies weighed down by an over-engineered stack and persistent data silos. Every company faces its own set of GTM challenges, but the path forward is largely the same: audit the workflows and tools to find the friction, then build the leanest solution that fixes it.

My current focus is workflow automation, leaning heavily on agentic AI to build, test, and deploy solutions quickly. The old way of doing operations is becoming obsolete — stitching broken systems together by hand no longer gives a company any durable edge in getting its product to market. The work now is clean, efficient data pipelines landing in a single warehouse, with the reporting layer on top: agents running research, analysis, and cleanup on the back end, and the GTM team working directly with those agents to surface reports, make the human-in-the-loop calls, and orchestrate the high-level work that used to be slow and manual.

Building systems like these takes both halves of the job: someone who understands the GTM engine and what it takes to sell, to decide what gets built and in what order, and someone with the technical skills to wire everything underneath. Years on a sales floor left me impatient with friction and gave me an instinct for building GTM systems that run themselves. This unique mix of firsthand sales experience and technical depth is hard to find in one person.

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05

Contact

If you've got a pipeline or a stack that isn't doing what it should, get in touch.

Get in touch github.com/jross21 LinkedIn Résumé ↓