Ref Id: HREZ-13806
Published Date: Jul 1, 2026
Job location: Costa Rica, Brazil, Colombia
General information
Description
HATCHWORKS AI Senior Full Stack Engineer Full Stack · Data Engineering · AI/ML · Remote / Costa Rica, Brazil, Colombia |
Remote / Costa Rica, Brazil, Colombia | 5+ Yrs Exp. | English Required | Full-Time |
THE ROLE
Most full stack roles are either frontend-heavy or backend-heavy. This one goes end to end — and the end includes the data layer.
You'll build features from the React interface all the way through the backend services, data pipelines, and warehouse that power them — and increasingly, the AI and ML capabilities layered on top. Intelligent candidate matching. Recommendations. Generative tooling that creates real, measurable value for users. Not a demo. Not a POC. A production system that people actually use.
This is a full stack role with a serious data engineering edge and a growing AI focus. The tech stack includes React and TypeScript on the frontend; Kotlin and Spring Boot on the backend; Airflow, DBT, Looker, AWS Redshift, DynamoDB, MongoDB, and Kubernetes across data and infrastructure; and a rapidly evolving AI/ML layer built on LLM APIs, embeddings, and vector search.
The engineers who thrive here are equally comfortable shipping a polished frontend, designing a clean API, reasoning about data at scale, and integrating AI into real products. If that describes you — read on.
WHAT YOU'LL ACTUALLY DO
- Build end-to-end features — design and ship user-facing functionality in React/TypeScript, the backend services behind it (Kotlin and Spring Boot), and the data that powers it. Own features from interface to pipeline.
- Develop and maintain backend services and APIs that process and serve data to our applications, with an eye toward performance, reliability, and clean contracts between systems.
- Design, build, and optimize data pipelines and workflows using Airflow and DBT to orchestrate and transform data across the analytics platform. This is the data engineering edge that sets the role apart — you'll be building applications on top of data you also help shape.
- Build AI-powered features — integrate LLMs and ML models for intelligent matching, recommendations, and generative tooling. Own the full lifecycle: prompts, retrieval pipelines, embeddings, vector search, and shipping these capabilities into real user-facing features.
- Manage and tune data storage systems including AWS Redshift and NoSQL databases (DynamoDB, MongoDB) to ensure data is organized, high-quality, and performant at scale — and ready to feed AI/ML systems.
- Monitor, troubleshoot, and improve reliability across the full stack — set up alerts, debug across application and data layers, and ensure features and data flow seamlessly from source to destination.
- Collaborate with product managers, designers, data analysts, and engineers to ship features that are intuitive, data-accessible, and decision-driving (including powering dashboards in Looker).
- Mentor and raise the bar — as a senior engineer, review code, share knowledge, and contribute to continuously improving engineering practices across the team.
THE TECHNICAL BAR
You will be expected to own features end to end from day one. The requirements below reflect the depth the role actually demands — not a wish list.
Must-Haves | |
Full Stack | 5+ years building full stack, data-intensive applications; hands-on with React/TypeScript (frontend) and Kotlin/Java or equivalent (backend); comfortable owning a feature across the whole stack |
Backend / JVM | Kotlin or Java with Spring Boot (or comparable framework); Python for data processing is welcome alongside |
Data Engineering | Built or maintained ETL workflows with Apache Airflow or similar schedulers; comfortable designing transformations with DBT or SQL — this is the edge that sets the role apart |
SQL & Databases | Strong SQL; experience with data warehouses (AWS Redshift or similar like Snowflake) and NoSQL databases (MongoDB, DynamoDB); can design efficient schemas and optimize complex queries |
Cloud & DevOps | Deploying and running applications in AWS; familiarity with containers and Kubernetes is highly valuable |
AI/ML Integration | Hands-on integrating LLMs or ML models into production applications — LLM APIs, prompt engineering, embeddings, vector databases, or RAG features. We care about shipping AI that works for users, not research science |
Communication | Works effectively on a remote team; explains technical concepts to both technical and non-technical colleagues; thrives in a collaborative, egoless environment |
NICE TO HAVE
- Looker or other BI tool experience — making data analytics-ready and accessible
- Data modeling and analytics architecture background
- Experience in recruitment technology, marketing data, or similar data-intensive SaaS environments
- A background that spans more than one layer — e.g. engineering + data science, or backend + ML
WORKING CONTEXT
This is a remote-first, fully distributed role. You will work across the US and Latin America, collaborating with product managers, designers, data analysts, and other engineers using modern async-first tooling.
We value autonomy and ownership. You will have the freedom to approach problems your own way and the responsibility to follow through on them. When you see an issue, you fix it. When you have a better approach, you propose it and own the outcome.
No egos. Code reviews, pair programming, and design discussions are about growing together — not pointing fingers. Senior and junior engineers learn from each other here.
THE HUMAN BAR
Ownership is not a perk. It's the expectation.
The engineers who thrive here are curious, dependable, and genuinely full stack in how they think — not just in their job title. You don't hand off to a data engineer when the query gets complex. You don't stop at the API boundary when the frontend feels wrong. You see the problem through to a clean result.
- Curiosity — you're always asking questions, exploring new technologies, and finding better ways to build. The AI/ML layer in this stack is evolving fast and that excites you, not unsettles you.
- Dependability — you take responsibility for your work, follow through, and can be counted on to deliver. When you see an issue, you fix it proactively.
- Collaborative — you explain technical tradeoffs clearly to non-technical colleagues and thrive where engineers, product, and data teams work closely together toward a shared goal.
- Problem-solving instinct — you're comfortable digging into logs, tracking down inconsistencies across the stack, and finding elegant solutions without needing to be told exactly where to look.
WHAT MAKES THIS DIFFERENT
This is not a role where you own one layer. You own features end to end — interface, services, pipelines, warehouse, and AI capabilities. That breadth creates leverage, and leverage means your contributions have a disproportionate impact on what the product can do.
The data engineering component is not a side note. It is the edge that sets this role apart. You'll build applications on top of data you also help shape, which means you understand your system fully — from where the data originates to how it renders on screen.
The AI/ML layer is real and growing. This is not AI theater — integrating LLM APIs, building RAG pipelines, and shipping intelligent features into production. You will be working on problems where a working deployment creates measurable value, and you will own it through to the outcome.
YOU'LL WORK INSIDE A PROPRIETARY AI DELIVERY FRAMEWORK
Most teams using AI tools today are flying blind — they feel faster but can't prove it. We built GenDD (Generative Driven Development) to change that, and you'll be working inside it from day one.
GenDD is a proprietary methodology and toolset built around one belief: great engineers need full context to move fast without breaking things. So we built the tools to give them exactly that.
Zero Ramp-Up | Proven Value | All-In Team |
Our tooling surfaces full project context — architecture, data flows, history — so you hit the ground running on every engagement. | GenDD instruments every agent interaction, commit, and deploy — turning your work into clear, client-ready evidence of real impact. | Everyone here uses agents seriously. You're not evangelizing — you're exploring. We learn fast, share everything, and enjoy the work. |
This is the environment engineers thrive in: structured methodology, tooling that solves real problems, teammates who move at pace, and work that matters at scale.
COMPENSATION & SETUP
Contract Type | Full-Time, Nearshore |
Location | Remote / Costa Rica, Brazil, Colombia |
Time Zone | Client timezone alignment required — LatAm preferred |
Experience | 5+ years in full stack software engineering |
Language | English required — Spanish is a plus |
HOW TO APPLY
Send us three things:
1. Why this role specifically — A brief note (3–5 sentences) on what draws you to full stack work with a data engineering edge. Be specific — we can tell the difference between someone who thinks in systems and someone who just reads job descriptions.
2. Something you've built end to end — A feature, system, or pipeline you owned from UI to data layer. Tell us what the problem was, what you built, and how it performed.
3. Your resumé or LinkedIn profile.
We read every application. We respond to the ones that show us you've actually done this.
HatchWorks AI is an equal opportunity employer. We evaluate candidates on technical merit, communication, and demonstrated capability — not credentials, geography, or background.
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