Data Engineer

Description

Job Summary: We are seeking a Data Engineer with an analytical mindset to scale our data infrastructure and optimize complex models. Key Highlights: 1. Maintain, optimize, and scale the data infrastructure. 2. Load analytical data and integrate AI into the Data Warehouse. 3. Transform heavy queries into efficient and clean ones. **Orquest** was born from a conviction: in the retail sector, every interaction matters. A fundamental part of the in-store experience lies in people — both customers and employees — and our software is specifically designed to synchronize both. We put **AI and machine learning** at the service of stores so they always have the right employee, in the right place, at the right time. Our data team is growing! Following the successful launch of our **Data Warehouse** as a standalone product, we are looking for a **Data Engineer** with the vision of a data analyst to keep our infrastructure clean, organized, and perfectly scalable. If you're passionate about optimizing complex data models and want to see the direct business impact of your pipelines, this is the place for you! ✨ How You’ll Make a Difference: * **Master the data "backstage":** You will maintain, optimize, and scale our data infrastructure to ensure a single, trustworthy source of truth for the entire company. * **Accelerate our Data Warehouse:** You will take ownership of loading analytical data and actively participate in integrating AI solutions into the warehouse ecosystem. * **Transform transactional into analytical:** You will optimize heavy and complex queries into clean and efficient ones. * **Support frontline business teams:** You will respond to ad-hoc requests and support our Customer Success and Adoption teams, ensuring strategic customers receive flawless analytical reports. * **Navigate growth with critical thinking:** You will bring order during build phases, rapidly adapt to shifting requirements, and proactively raise your hand to propose infrastructure improvements. **Requirements** What We’re Looking For: * **Mid-to-Senior Experience:** Approximately **4 or 5 years of professional experience** in data-related roles. It is highly desirable that you began your career as a *Data Analyst* before transitioning to *Data Engineer*, ensuring you understand the business impact behind the data you extract. * **Expert SQL Proficiency:** Expert-level SQL skills are absolutely essential. You must possess a robust logical structure for data modeling and maintenance. * **Pipeline Architecture:** Deep understanding and design capability for ETL processes and optimization of large-scale databases. * **Attention to Detail:** A cross-tabulation error in a report delivered to an end customer could be critical for the company. We value rigorous, methodical, and meticulous execution across everything you deliver. * **Tech Stack & AI:** Strong logical programming ability in **Python**. Familiarity with the **AWS Cloud environment (specifically Glue and S3)** is recommended, though equivalent experience on Azure or GCP is welcome if you demonstrate high learning velocity. We expect you to feel comfortable leveraging AI assistants (e.g., Claude) to optimize your daily efficiency. * **Proactivity & Communication:** You will join a flat, highly collaborative team of four people. We do not seek isolated profiles; we look for someone with a natural inclination to coordinate solutions across departments and communicate blockers with full transparency. * **Languages:** Native or fluent **Spanish** is mandatory. A professional level of **English** is not an exclusionary requirement, but it’s great if you have it. **Benefits** What We Offer: * ✨ A key role in a fast-growing international SaaS company. * Fully **remote** work model. * A flat team with cross-support dynamics and no rigid hierarchies. * Real ownership and autonomy to drive impact. * **Beyond Salary:** * + Comprehensive health insurance. + OpenUp platform for your mental wellbeing. + Cobee for flexible compensation (meals, transportation, etc.). + Language classes.