It has been 100 days since I became a GTM Engineer.
This is the first role I’ve taken up where product management, engineering, revops, and marketing overlap almost every day.
People often ask me what I actually do. It’s hard to explain, so I figured I might as well show what a typical day in my life looks like:
6:00 AM - The previous day’s marketing and sales activities are mapped to each account in our account journey table. This table powers most workflows
6:30 AM - Intent, engagement, and signal scores are recalculated, highlighting accounts whose scores increased overnight.
7:00 AM - LinkedIn does not share reply data and hence a workflow records the prospect replies shared by our team in Slack and extracts details such as the commission owner and current tool.
9:00 AM - A competitive intelligence digest lands in Slack, covering website updates, LinkedIn activity, job changes, and new advertisements.
10:00 AM - Replies from every outbound automation are consolidated into one table for review.
11:00 AM - Yesterday’s conversations with Clara, our Storylane website assistant, are pushed to Snowflake and classified as sales-worthy or noise.
This is usually when I start my workday. Quite a bit has already happened without my intervention.
2:00 PM - The team receives one “surprise account” based on intent, engagement, signals, and outreach history, along with recommended actions.
4:30 PM - Factors.ai sends us paid and organic LinkedIn engagement data, which is added to the account journey table.
5:00 PM - OpenFunnel (YC F24) shares company signals that are pushed to a Sigma dashboard used by RevOps to enrich accounts and identify outreach contacts.
5:15 PM - Our BDR performance dashboard on Railway refreshes with accounts touched, calls, connect rates, meetings, sales-qualified leads, and upcoming conversations.
5:30 PM - As the BDR team logs in, each person receives a Slack update comparing their week-to-date performance with last week and their 12-week average.
5:30 PM (My favorite) - Every SQL created the previous day is traced back through its account journey. We document the personas engaged, channels used, and steps that led to the opportunity in Confluence, then post two updates in Slack: a shoutout to the BDR who sourced it and the gaps the team should fix.
5:45 PM (Most Optimistic) - Our lemlist automation powered by Trigger.dev qualifies yesterday’s signals, creates personalization variables, selects contacts, and enrols them into the right sequences.
6:00 PM - A GitHub Action identifies accounts that should have received outreach but did not, then updates Slack and Sigma for the BDR team.
After 100 days, my biggest learning is that the role is rarely about completing each task manually. It is about building systems that keep completing them.
All of this is possible because of 8+ years in analytics consulting, marketing analytics, and growth, plus a lot of Claude Code. :D
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