Eneba is looking for an experienced Data Engineer to join its remote Data Platform team. In this role, you will help build scalable data infrastructure and high-performance data pipelines that empower analytics, machine learning, and data-driven decision-making across the organization.

You will collaborate with data engineers, analysts, and machine learning specialists to create efficient large-scale data processing systems, improve automation workflows, and optimize data delivery pipelines. This opportunity is ideal for engineers passionate about cloud-scale data platforms, distributed processing, and modern data engineering practices.

• Design, build, and maintain scalable data pipelines to ensure reliable and efficient data access.
• Collaborate with machine learning engineers to automate training, quality assessment, and model release workflows.
• Develop data infrastructure for analytics, experimentation, and company-wide metrics.
• Optimize data delivery processes and automate manual operational workflows.
• Design and improve data modeling patterns and implement scalable data architecture solutions.
• Process and manage large-scale datasets using distributed computing technologies.
• Contribute to maintaining a strong data-driven culture across the organization.
• Collaborate cross-functionally with analytics, ML, and engineering teams.

• 5+ years of experience in data engineering or related roles.
• Strong experience with Apache Spark using Python for large-scale data processing.
• Experience working with cloud data platforms such as Databricks or Snowflake.
• Experience with cloud infrastructure platforms such as AWS, Azure, or Kubernetes-based environments.
• Hands-on experience using Git for version control and collaboration.
• Strong SQL knowledge for querying and managing relational databases.
• Experience designing scalable and maintainable data pipelines.
• Strong analytical thinking and problem-solving abilities.
• Ability to collaborate effectively in cross-functional technical teams.