Remote Data Engineer — Scale Pipelines & ML Workflows

Eneba; Polska; Remote; PLN 240,000 - 314,000; Full time

Benefits offered by this job

Employee Stock Options

Bonus programs: performance-based, etc

Flexible location: office or remote

Learning budget

Job summary

Eneba is building a data-driven marketplace for gamers, with a team powering data infrastructure to enable actionable insights across the organization. The data team focuses on pipelines, ML collaboration, and scalable data platforms to support analytics and decision making.

The role involves designing and maintaining data pipelines, collaborating with ML engineers on training and model release, and building robust data infrastructure to support analytics and company metrics.

Qualifications

• 5+ years of experience in data engineering or a similar role.
• Extensive experience with Apache Spark using Python for large-scale data processing.
• Familiarity with Databricks or similar cloud data platforms (such as Snowflake) would be a significant advantage.
• Experience with AWS, Azure, or other Kubernetes platforms is a huge plus.
• Must have hands-on expertise with Git for version control and collaboration.
• Solid knowledge of SQL, essential for querying and managing relational databases.

Responsibilities

• Enable efficient data access by creating and maintaining data pipelines.
• Collaborate with ML engineers to design and maintain automation for machine learning training, quality assessment, and model release process.
• Build data infrastructure from the vast amount of data for analytics, hypothesis testing and company metrics.
• Identify, design and implement improvement to internal processes allowing to optimize data delivery, automate manual processes.
• Design new and improve current patterns for building data models and implement necessary modifications.

Skills

Data engineering

Python

Spark

Data modeling

Tools

Databricks

Snowflake

AWS

Azure

Kubernetes

Git

Job description

Eneba is building a data-driven marketplace for gamers, with a team powering data infrastructure to enable actionable insights across the organization. The data team focuses on pipelines, ML collaboration, and scalable data platforms to support analytics and decision making.

The role involves designing and maintaining data pipelines, collaborating with ML engineers on training and model release, and building robust data infrastructure to support analytics and company metrics.