Data Engineer

Workana is the largest remote work platform for talent in Latin America. Our division focuses on matching exceptional professionals with leading and innovative companies around the globe.

About the client

Our client is a fast-growing life sciences and biotechnology company operating at the intersection of computational research, data platform engineering, and advanced analytics. They develop production-grade data systems designed to support complex scientific research, clinical trial analytics, and machine learning workloads. We are looking for an experienced Senior Data Engineer to join their engineering team and take technical ownership of their data platform architecture.

Role overview

As a Senior Data Engineer, you will own the end-to-end architecture, design, and implementation of scalable data platforms and processing pipelines. You will work directly with scientists, researchers, and machine learning engineers to translate complex domain requirements into technical solutions that power downstream analytics and production ML systems.

We are looking for a senior technical owner who can operate autonomously, drive best practices across distributed processing and data modeling, and collaborate effectively with international stakeholders in an English-speaking environment.

Selection process

The process usually consists of 4 stages and takes on average 4 weeks to be completed, depending on eventual additional technical rounds or take-home tests.

• Initial interview with Workana's recruiting team;
• Technical interview with Workana's technical team;
• Client Cultural fit interview;
• Final interview with C-level.

As Workana has multiple clients, if you pass the first round with Workana's recruiting team, you may also be considered for other relevant opportunities if the initial opportunity does not move forward.

Responsibilities

• Architect, build, and maintain production-grade data platforms and scalable ELT/ETL pipelines.
• Ingest, transform, and model complex structured and unstructured scientific and clinical datasets.
• Define data architecture patterns, engineering standards, and best practices across the team.
• Collaborate with scientists, ML engineers, and business stakeholders to turn domain needs into data solutions.
• Design data infrastructure that supports machine learning training, inference, and analytics workloads.
• Ensure data quality, lineage, reproducibility, security, and system observability.