Data Engineer

The Data team is one of the newest additions at Infinum, focused on delivering impactful solutions in data engineering and artificial intelligence. We work on a range of client projects, helping turn data into real business value.

Our work spans everything from building robust data pipelines to developing AI-powered systems that extract insights and enable smarter decision-making. Right now, we're building machine learning algorithms that recommend relevant content to users of an IoT app, along with data pipelines that power analytics and ML models behind the scenes.

As a growing team, we're looking to significantly expand in the near future. We often collaborate with DevOps, Backend, QA, and System Architect teams and have delivered data-based solutions for industries like smart devices, hospitality, and healthcare. If you’re excited by complex challenges, enjoy collaborating across disciplines, and want your work to make a real impact in industries that matter, come build with us. We’re growing fast, learning faster, and always up for a cup of coffee and a great idea. 

Requirements

• at least 2 years of experience as a Data Engineer
• experience with scalable and production-ready distributed data systems (SQL/NoSQL) and cloud-based data solutions
• proficiency in Python (preferred) for designing robust data solutions and AI integrations
• experience with ETL tools and efficient, secure data pipelines
• strong grasp of data lakehouse concepts, including modeling and performance
• experience with Databricks or similar platforms for collaborative data and AI development, ideally with at least one year of hands-on experience
• familiarity with Apache Spark, Snowflake, or Microsoft Fabric
• hands-on experience with AWS, Azure, or GCP for cloud-native infrastructures
• broad knowledge of design patterns for scalable, maintainable code
• experience working independently and collaboratively in agile environments (Scrum, Kanban)
• excellent communication skills, with the ability to explain complex technical concepts to non-technical stakeholders
• fluency in English

Bonus Points

• solid understanding of LLM, prompt engineering, RAG architectures, and other similar AI concepts
• familiarity with integrating pipelines with AI models and deploying AI features
• knowledge of vector databases, graph databases, experience with real-time streaming pipelines
• exposure to Kafka and Kubernetes

Your responsibilities

• designing and implementing high-quality data architecture and software solutions