I build the systems between a signal and a revenue-team action
I am a GTM Engineer at Everstage in Chennai, India. My work sits where analytics, automation, AI, and day-to-day revenue execution meet.
A useful GTM system does more than calculate a score or produce a dashboard. It notices something important, decides who should act, puts the right context in front of that person, and records what happened next.
That is the operating definition I use. I build scoring and prioritisation systems, activity-gap views, intent reports, workflow monitors, and automations that turn fragmented GTM data into a clear next action.
From analyst to GTM engineer
I spent 8+ years in analytics consulting, marketing analytics, and growth before taking the GTM Engineer title. That path matters because a GTM engineer has to understand how a BDR works a queue, how a marketing team defines a qualified response, how leaders inspect pipeline, and how imperfect data travels between systems.
At ZoomRx, I built a Python algorithm that cut pharma-conference planner generation from 12 hours to 20 minutes. At Freshworks, I moved through three roles (Senior Marketing Analyst, Lead Marketing Analyst, then Lead Growth Analyst) across marketing analytics and growth: more than 70 website experiments contributed $1.2 million in incremental ARR and a five percent lift in lead volume. I also managed analytics behind a $30 million inbound marketing budget and built a model that helped remove roughly $1 million in ineffective spend.
At Atlan, the work moved closer to the modern GTM-engineering shape. I built the data foundation for growth reporting, account and lead scoring, lifecycle classification, attribution, more than 40 dashboards, and more than 20 AI-assisted workflows using tools such as n8n, Claude Code, Snowflake, and Sigma.
Today at Everstage, I build AI-native GTM systems for outbound execution. Public examples include account-scoring refreshes, engagement-intent reports, BDR performance tracking, outreach-gap detection, an AI reply-handling pipeline with human approval in Slack, GTM agents tested with evals, and a RAG knowledge base that grounds their answers.
What I build
- Signal and scoring systems: combine fit, engagement, and sales activity into prioritised account views.
- GTM automations: move data and context across the stack without relying on repeated manual work.
- Action dashboards: make a decision obvious instead of adding another reporting surface.
- AI-assisted workflows: use language models inside bounded processes with explicit inputs, outputs, and review points.
- Operational monitors: show whether scheduled jobs ran, failed, arrived late, or never started.
- Personal agent systems: connect captures, notes, learning material, and daily briefs through webhooks and scheduled workflows.
You can see concrete examples on my work page, including a cron monitor spanning seven repositories, the Hermes personal-agent system, and a daily work-memory agent.
The tools are not the strategy
My current toolkit includes Claude Code, n8n, Trigger.dev, GitHub Actions, HubSpot, Snowflake, Sigma, Python, and SQL. I choose tools based on the workflow.
The hard questions come first:
- What decision should change?
- Which signal is reliable enough to trigger action?
- Who owns the action?
- What context do they need at that moment?
- How will we know the workflow ran and was used?
- What happens when the data, model, or automation is wrong?
The AI GTM engineering guide explains how those pieces fit into one production system.
Why India is an advantage
India has deep analytics, engineering, SaaS, and operations talent. The GTM Engineer title is newer than those disciplines, but the underlying work is familiar: connect data, systems, and commercial execution without losing ownership between teams.
My background reflects that convergence. I studied computer science, built a career in analytics, moved into growth, and became a builder when AI coding tools reduced the distance between an operational idea and production software.
That makes the role useful to two audiences. A B2B SaaS founder gets someone who can translate a revenue problem into a working system. A hiring manager gets an operator who can work across data, process, tools, and adoption rather than optimising one isolated layer.
What good GTM engineering looks like
Good GTM engineering is inspectable. The system has a named user, an explicit action, a failure path, and a measurable operating outcome.
It does not need to be fully autonomous. In many revenue workflows, a human review point is a feature. The goal is to remove repetitive assembly work while preserving judgment where context, brand, or customer impact matters.
It also survives after the demo. Credentials expire. Schemas change. schedules drift. APIs rate-limit. Teams change their process. A production workflow needs monitoring, documentation, and a clear owner.
My writing focuses on systems and failure modes. Start with what GTM engineering means, explore the career path in India, or read why GTM engineering and RevOps are complementary.
Work with or evaluate me
If you are building an AI-native revenue system, hiring for a GTM engineering role, or comparing approaches to a difficult workflow, the fastest route is to inspect the work and ask specific questions.
Browse the production and open-source projects, review the field notes, visit GitHub, or book a call.
Frequently asked questions
Who is a GTM engineer in India?
Vikas Pareek is a GTM Engineer at Everstage in Chennai, India. He builds production revenue systems across analytics, automation, AI, and B2B SaaS operations, after 8+ years in analytics consulting, marketing analytics, and growth at ZoomRx, Freshworks, and Atlan.
How do I become a GTM engineer in India?
Most people arrive from analytics, growth, RevOps, or engineering, then add the missing side: commercial context or the ability to build. The career path in India covers the skills and a practical route in.
What does a GTM engineer build?
Scoring and prioritisation systems, GTM automations, action dashboards, AI-assisted workflows, and operational monitors that show whether scheduled jobs ran. Concrete examples are on the work page.
Is GTM engineering the same as RevOps?
No. RevOps usually governs process, systems, and definitions, while GTM engineering builds and operates the workflows that cross those layers. The two work as partners, as explained in GTM Engineer vs RevOps.