RSA (Resident Solutions Architect)

About the company

A nearshore software engineering and AI services firm with Uruguayan roots. It was formed in 2023 through the merger of two leading Uruguayan software development companies, backed by the New York investment fund Recognize. It has since grown to around 1,100 professionals, partly by acquiring a Uruguayan AI pioneer with 16 years of experience. It is headquartered in Austin, Texas, and has offices and delivery centers across the US and Latin America. The firm positions itself as a partner helping businesses move from digital-native to AI-native, with services spanning AI-accelerated software engineering, industry-specific AI agents for sectors like financial services, healthcare, media, and manufacturing, and end-to-end data infrastructure. Its client base includes enterprise names such as Pluralsight, Unilever, Shopify, and Google, and clients consistently highlight its technical expertise, strong project management, and high satisfaction and retention.

About the role

We're looking for a Senior Data Engineer with strong experience in data engineering, data platforms, and analytics to work on high-impact projects involving Databricks, Apache Spark, and cloud technologies.

This role requires hands-on experience designing and developing solutions on Databricks, combined with a strong understanding of distributed computing, performance optimization, and scalability.

The ideal candidate has a strong consulting background and experience delivering multiple data engineering projects, with the ability to work independently and contribute to both technical implementation and solution design.

Responsibilities

• Design, develop, and implement Data Engineering solutions using Databricks and distributed computing technologies.
• Work hands-on across data engineering projects, from development through production deployment.
• Build and optimize data pipelines and processing workflows with a focus on performance, scalability, and reliability.
• Develop solutions using Apache Spark, applying best practices for distributed data processing and optimization.
• Work across multiple cloud ecosystems and leverage cloud services to build scalable data solutions.
• Implement and maintain CI/CD processes for production deployments.
• Apply MLOps practices where required by the project.
• Stay current with the broader range of Databricks products, features, and platform capabilities.
• Identify performance bottlenecks and recommend improvements to existing data solutions and architectures.
• Collaborate with technical and business stakeholders across Data Engineering, Data Platform, and Analytics projects.
• Contribute technical expertise and best practices across different client environments and projects.

Requirements

• 7+ years of experience in Data Engineering, Data Platforms, and/or Analytics.
• 10+ years of consulting experience, or equivalent experience working across multiple projects and client environments.
• Hands-on development experience across at least 6–8 Databricks projects.
• Deep experience with Apache Spark and distributed computing, including knowledge of Spark runtime internals.
• Working knowledge of at least two cloud ecosystems among AWS, Azure, and GCP, with deep expertise in at least one.
• Familiarity with CI/CD practices for production deployments.
• Working knowledge of MLOps concepts and practices.
• Current knowledge of the broader Databricks product and platform ecosystem.
• Experience optimizing data solutions for performance and scalability.
• Completed Data Engineering professional certification and the required coursework.
• Strong problem-solving skills and the ability to work independently in complex technical environments.

Benefits

• People First culture.
• Free access to streaming platforms.
• Free access to Spotify premium.
• GYM discount.
• Travel discount.
• E-Learning discount.
• Birthday-day