Experienced Data Engineer

Keba d.o.o.

4.7

Odgovara na prijave

Novi Sad

Novi Sad: Hilandarska 2

30.07.2025.

SQL Python Docker intermediate

Who are we

KEBA Novi Sad is founded in 2012, as a subsidiary of the Austrian company originally founded in Linz.

After more than fifty years on the market of industrial automation, KEBA Group today reaches several continents and has a wide portfolio of products and area of interest. Alongside Industrial Automation as the major business area, some of our other divisions are banking automation, energy automation, electro-mobility, logistics, etc.

Note: Our company's work mode is hybrid, with only 1 guaranteed day of Home Office work per week - more is negotiable later on.

What would you do in KEBA

We are seeking a new team member with proven experience in data engineering. As a Data Engineer, you will join our growing data team in Novi Sad, focused on establishing core competence in data engineering to support our innovative data-driven initiatives.

You will work on building and maintaining scalable data infrastructure, playing a key role in enabling efficient data integration and analytics across the organization. Your work will involve designing end-to-end data pipelines, developing robust ETL processes, and implementing data governance practices to ensure high data quality and consistency.

You will collaborate closely with domain experts and data analysts to understand business needs and deliver impactful solutions. In this agile and interdisciplinary environment, you’ll also contribute to the design of data models and both cloud-based and on-prem architectures.

While hands-on experience with cloud platforms and data orchestration tools is a plus, a solid foundation in data engineering and a proactive, solution-oriented mindset are most important.

Additionally, you may have the opportunity to work on other related projects within our company as they arise.

Responsibilities:

► Data Pipeline Development

Design, implement, and optimize end-to-end data pipelines for ingesting, processing, and transforming large volumes of structured and unstructured data.
Develop robust ETL (Extract, Transform, Load) processes to integrate data from diverse sources into our data ecosystem.

► Data governance

Implement data validation and quality checks to ensure accuracy and consistency.
Catalog data products and source systems.
Technical guidance in data governance for domain experts.

► Data Modeling and Architecture

Design and maintain data models, database schemas, and database structures to support analytical and operational use cases.
Optimize data storage and retrieval mechanisms for performance and scalability.
Evaluate and implement data storage solutions, including relational databases, NoSQL databases, data lakes, and cloud storage services.

► Collaboration and Documentation

Collaborate with data scientists, analysts, and other stakeholders to understand data requirements and deliver tailored solutions.
Document technical designs, workflows, and best practices to facilitate knowledge sharing and maintain system documentation.