Software Engineer, Data

👋 About TextYess

For thousands of years, commerce was human. Merchants knew their customers, understood their needs, and built trust through real conversations. Then came the internet, and we traded humanity for scale. Shopping became efficient but soulless: endless product grids, generic descriptions, no one to guide you.

TextYess is here to restore what was lost. We build AI agents that bring back the personal, conversational shopping experience, at infinite scale. Through WhatsApp and messaging apps, we help brands sell through tailored, real-time conversations that feel natural, helpful, and human.

We work with brands like Ducati, Pittarosso, Piazza Italia, and Doppelgänger. We closed a €2.4M seed round and we're just getting started.

🚀 The Role

At TextYess, the data platform is deeply embedded in the product, the infrastructure, and the integrations — you can't work on one without touching the others. You're an engineer who works on data problems, not a data person who learned to code. Your day might start with a dbt model, move to a Terraform module, and end with a product feature that surfaces what you built to merchants. The split between data, DevOps, and product work isn't fixed — it shifts with what the company needs, and we want someone who's comfortable with that.

You'll be the second person on the data team, working alongside Giuseppe (Data Lead) in a growing engineering team led by Valdo (CTO). The team is all internal, ships weekly, and is expanding. We're remote-first with an office in Milan.

TextYess processes millions of conversations, orders, and product catalog events across 270+ active merchants. Behind all of it is a data platform that powers analytics, AI agent intelligence, and the merchant-facing dashboards our customers rely on every day. Today, that platform is built on AWS, dbt and Terraform — and it's run by one person.

That's about to change. You're here to make the data platform production-grade at the next level: reliable ingestion, real-time observability, and the infrastructure to support a product that's scaling fast. And because data doesn't exist in a vacuum, you'll work across the full product — understanding how merchants use TextYess, how orders flow through integrations, and how the AI agents consume the data you build.

We're an AI-native team: coding agents, agentic workflows, and automated review are part of how we work every day. You start your day opening Claude Code. You run parallel sessions. You don't write boilerplate — you design, review, and direct. If that's not already how you operate, this isn't the right role.

What you'll work on:

→ Own and improve the data ingestion layer — CDC and ETL pipelines, source reliability, alerting

→ Build and maintain dbt models across staging, intermediate, and marts layers

→ Design observability for the data platform — freshness monitoring, pipeline health, anomaly detection

→ Work on infrastructure — Terraform, AWS, CI/CD for the data stack

→ Contribute to product features that depend on data — merchant dashboards, intelligence surfaces, analytics

→ Help architect the next-generation data platform — we're evaluating a migration to a faster, more cost-effective stack

→ Build integrations with e-commerce platforms, payment systems, and messaging channels

🎯 Who You Are

Maybe you started as a software engineer and got pulled into data because you wanted to understand what your code was actually producing. Or maybe you started in data and got frustrated by only writing SQL — you wanted to build the systems, not just query them. Either way, you ended up at the same place: an engineer who's equally comfortable with a pipeline and a product feature, and who refuses to be boxed into one thing.

You've built and maintained data systems that real products depend on. You know the difference between a pipeline that works in dev and one that holds up when a source silently stops replicating for days. You think about the product and the business, not just the data. You write code like a software engineer, not like someone who learned Python to run Spark jobs. You move fast, you own what you build, and you don't wait to be told what to fix.

Must-haves:

→ Strong software engineering skills — Python, SQL, and comfortable writing production code beyond scripts

→ Experience with data pipelines — ETL/ELT, ingestion, transformation, orchestration

→ Infrastructure and DevOps chops — Terraform, AWS, CI/CD. You can manage what you build.

→ Experience shipping features in digital products — you've worked end-to-end, and you understand that data serves a product

→ Extreme ownership — you carry things to completion without being pushed

→ Fluent with coding agents — Claude Code, Cursor, or equivalent is your default environment

Bonus:

→ Experience with modern analytics engineering — dbt, cloud warehouses, platform migrations

→ E-commerce or marketplace data — orders, catalogs, customer events

→ Experience with real-time data or streaming architectures

→ Previous startup experience — you know what urgency feels like