Data Engineer, Epignosis, GR

At Epignosis, we build workplace learning software that enables more than 70,000 teams worldwide to offer training to millions of people. Our portfolio of tools can be used by organizations of any size and includes TalentLMS - a zero-overhead cloud LMS, eFront - an enterprise LMS, TalentCards - a mobile app for training the deskless workforce, and TalentHR - a lightweight HRIS to manage people ops. We are competing with the world’s top eLearning solutions and we aim at becoming world’s #1.

We're on a mission to democratize training by developing simple, accessible, and affordable software. We count 12.000+ companies as customers and with more than 12 million users across 126 countries logging in to use our platforms, we are a much-loved global leader in learning tech. At home, we are quickly becoming one of the largest SaaS company to come out of Greece.

We are looking for a skilled Data Engineer to join our Data Team and play a key role in implementing our Data Transformation project. The ideal candidate will design, develop, and maintain scalable data pipelines and workflows to support analytics and business intelligence initiatives. Additionally, they will be responsible for managing data infrastructure, optimizing databases and schemas, and ensuring data integrity.

This role involves close collaboration with Developers, DevOps, AI Engineers and other cross-functional teams to optimize queries and databases, propose scalable architecture solutions, and identify data requirements that drive our services forward.

Responsibilities

As a Data Engineer, you will:

• Monitor and optimize database performance, implementing tuning measures while ensuring data integrity and security.
• Provide proactive and reactive data management support, including user training and troubleshooting.
• Work closely with developers to optimize queries and design efficient database schemas.
• Collaborate with AI Team to support AI-driven initiatives.
• Design, develop, and maintain ETL pipelines to efficiently process and integrate data from multiple sources.
• Partner with different team within the Engineering department to understand data needs and deliver data solutions.
• Implement data modeling techniques to create efficient schemas for relational and non-relational databases, such as Aurora, DynamoDB or other.
• Optimize data workflows and storage solutions to enhance performance and scalability.
• Ensure data quality and consistency through validation and cleaning processes.