Data & GTM Engineer · ex-Pieces, SuprSend

Growth systems your team wishes it had. Without the headcount.

For heads of marketing, growth, and founders at dev-tool and B2B startups.

I build the outbound, attribution, and content systems your roadmap keeps pushing to next quarter. Shipped and running. Not slideware.

Pieces full-timeOpenHands contractOpenObserve contractSuprSend first marketer

What I do

The systems that do the work nobody has time for.

Most teams don't need more tools. Or more headcount. They need the plumbing between what they already have. And someone who builds it, ships it, then leaves it running.

Pipeline

Outbound that finds real buyers

Find who's actually using your product. Or shopping your competitor right now. Enriched, drafted, dropped in your reps' inbox every morning.

Shipped: the Stark Agent + Iris copilot at OpenHands · a personalized demo engine at OpenObserve

Clarity

Attribution you can trust

One source of truth across your scattered tools. Finally answer which channels bring customers who stick. Not just who sign up.

Shipped: GA4 → Apache Beam → BigQuery pipelines · multi-touch attribution at Pieces

Leverage

Content & team memory

One post becomes native content everywhere. And your team gets instant recall of every decision, signal, and shipped feature.

Shipped: Osiris marketing RAG on GCP · a content multiplication engine · social-listening agents

Named systems, all built end to end and left running: Stark Agent, Iris, Osiris, the OpenObserve demo engine, and more below.


Recent client work

Already built this for companies like yours.

Independent GTM engineering for dev-tool companies. Same systems, delivered as consulting. Handed over runnable.


Where the systems come from

Five systems. One person. 18 weeks. At Pieces.

I owned marketing data and marketing AI at Pieces (Series A, $13.5M). Built five systems the growth team kept needing. No tool on the market solved them. Here's what each does for the business. The engineering is one click away.

Built at Pieces

Your team's memory

Ask what did we decide, what shipped last week, what are customers complaining about. Get a sourced answer in seconds. No more 15-minute hunt through chat, docs, and meeting notes.

min to find one answer15
seconds2.8
How it works (technical)

A retrieval system (RAG) over 50+ chat spaces, daily transcripts, GitHub, and product docs. Runs on Google Cloud. The hard part isn't search. It's relevance. Every retrieved doc gets graded before it's used, so answers don't mix last quarter's strategy with this quarter's pivot. Same knowledge base feeds the outbound and content systems below.

$ ask "What did the growth team decide about the Dev.to campaign?" The team decided to double down on Dev.to. Attribution showed 34% activation vs 12% from paid. Sources: Growth Sync (Jan 21), Campaign Review (Jan 18) Answer in: 2.8s (vs ~15 min of digging)

Built at Pieces

Always-on social listening

Finds the conversations worth joining. The dev quietly evaluating you. The comparison thread. The buying-intent question. Scored and dropped in Slack. The noise gets filtered out.

found by hand / day0–2
real ones / run12
How it works (technical)

A single tweet means nothing alone. “Just tried the copilot” could be praise or a complaint. The system walks the full reply chain, enriches the profiles involved, then scores the whole context with AI. Runs on a schedule from the cloud without getting IP-banned. Backs off when rate-limited. Falls back to sequential if parallel fails. No manual restarts.

Built at Pieces

Attribution that survives scrutiny

Answers the Monday question every growth lead has. Which channels bring customers who actually stick. Not just who sign up. One source of truth, pulled from tools that never talked to each other.

disconnected tools5
query for the answer1
How it works (technical)

The whole measurement stack, built from scratch. GTM tag management and event taxonomy. Apache Beam pipelines pulling web, social, and product-signup data into BigQuery. A lifecycle model joining session to feature usage to retention, with multi-touch attribution. Pipelines are idempotent, so a failed run retries without duplicates. New client means new config, same code.

sourcesignupsD7 retention ─────────────────┼─────────┼────────────── dev_to_blog │ 342 │ 34% twitter_organic │ 187 │ 28% linkedin_ads │ 523 │ 14% One query across 5 tools that never talked. The channel with the most signups had the worst retention.

Built at Pieces

Outbound that finds real buyers

Not stargazers. The people actually running your product in production. Or shopping your competitor today. Each one researched, scored, and handed to your reps as a draft. They review and send.

min research / lead15
min, evidence attached5
How it works (technical)

Signals from 9 platforms: GitHub, Docker, PyPI, Stack Overflow, job boards, competitor comparisons, and more. Linked into one identity per person. Scored for real production evidence: a config committed, a CI/CD workflow, an org-owned image. Replaces 3 to 4 tools (Apollo, Clearbit, reo.dev, Sales Navigator). Refuses to draft for low-confidence matches, so reps never spam someone who just starred a repo.

Lead: karan-sharma · Zerodha (India's largest broker) Verdict: Active production user · high confidence Why this lead is real: ├── Running the product in CI/CD ├── 11 commits in 2 months ├── Public project built on top (18★) └── Company confirmed via GitHub bio → Draft written. Rep just approves.

Built at Pieces · Live tool

One post, every platform

One blog post becomes native posts for Dev.to, LinkedIn, X, and Medium. Written to each platform's rules. Quality-scored. Deduped. Seconds, not the 2+ hours it takes by hand.

hours of repurposing2
seconds12
How it works (technical)

Every platform has different rules. Dev.to wants code depth. LinkedIn wants 1,300 professional characters. X wants a 280-character hook. The system scores each variant on a 100-point rubric and refuses to publish below 70. If two variants are more than 85% similar, it regenerates on its own. No daily babysitting.


What people who hired me say

References from the people I built it for.

Tsavo Knott

Nikhil led both marketing data infrastructure and marketing AI systems. I recommend Nikhil to teams building developer-focused products that need someone who can architect and execute technical GTM systems end-to-end.

Tsavo Knott, CEO & Technical Co-Founder, Pieces (Series A, $13.5M)

References available on request


How I got here

I started in marketing. Then got tired of waiting on systems that didn't exist.

Before Pieces I was the first marketer at SuprSend. New category, infra startup. No marketing ops. No RevOps. No data team. The campaigns worked. But every workflow I needed, I had to build myself.

That's when it clicked. The campaigns were never the bottleneck. The systems were. So I became someone who builds them. We didn't just rank on G2. We created the notification-infrastructure category and took #1.

See the SuprSend work →

Which Monday question is your team still answering on Wednesday?

Head of marketing, growth, or a founder who wants the systems built. Not just advised on. Let's talk. I take a few engagements at a time.