FullEnrich is a profitable B2B data enrichment platform with 3,000+ customers. We aggregate 20+ data providers into a single waterfall system that helps sales and marketing teams find the contact data they need. We're a small, fast-moving team with a product-led growth motion and strong presence in the US market. We're backed by great investors and growing fast.
We’re a remote-first team of 20+ people, distributed across the US, UK, and France, with teammates also in North Macedonia, Brazil, and Argentina.
We're looking for a curious & pragmatic, Internal Software Engineer to work directly with our CPO and co-founder.
Here’s the honest pitch: today, a lot of internal tooling, automation, tool setup, ad-hoc scripts, and data work is still handled directly by the CPO. That doesn't scale. We’re looking for someone to take ownership of our internal tool stack and productivity systems, from building AI-powered tools to automating repetitive processes, to supporting data-driven decisions across the company.
This is not a typical engineering role. You’ll not work on customer facing product. Instead, you'll work on short-cycle missions that change week to week. One week you might spend building an AI agent that answers questions from our data warehouse via Slack.The next, you might be automating a sales workflow using n8n, or setting up a new self-hosted dashboarding tool.
Your "customers" are internal: the product team, the sales team, the revenue operations team. You'll build for them, demo to them, train them, and make sure what you ship actually gets adopted.
Build internal AI agents and tools. For example: a Slack bot that lets anyone query our data warehouse in natural language ("I’ve a call in 15min with customer XYZ, brief me on what they have been doing on the platform"). Or an internal app where sales can drag-and-drop a security questionnaire, and an AI agent reads, answers each question, and returns the completed file with intact formating, while flagging questions it couldn't confidently answer.
Automate internal processes with n8n. Identify repetitive tasks across teams and build automations that save real time.
Support data analysis and decision-making. Work with dbt, our data warehouse, ETL & ReverseETL (Airbyte and HighTouch) to build metrics, and ad-hoc analyses, and push data to the right tools. You won't be a full-time data analyst, but you'll need to be comfortable with numbers and able to produce outputs (dashboards, reports) that support strategic decisions.
Explore and prototype AI use cases to improve internal productivity. You’ll have time to experiment with new approaches, test agent architectures (orchestration, sub-agents), and identify what we can build to make teams more effective.
Train and support adoption. Building a tool is half the job. The other half is making sure people actually use it. You'll demo what you build, onboard teams, and iterate based on feedback.