Data Engineer

About the role

We are seeking a Data Engineer to design, build and maintain scalable, secure data platforms that enable analytics, reporting and AI/ML initiatives across the enterprise. The role works in a hybrid model from Novi Sad or Belgrade and collaborates closely with Data Scientists, AI Engineers, platform teams and business stakeholders.

Key responsibilities

• Design, develop and maintain scalable ETL/ELT pipelines for structured and unstructured data.
• Build and support modern data architectures including data lakes, data warehouses and real‑time streaming platforms.
• Ingest, integrate, process, transform, validate and enrich data from internal and external sources while ensuring high data quality.
• Design and maintain data models that support analytics, reporting and AI/ML use cases.
• Provide curated datasets and support feature‑engineering initiatives for Data Scientists and AI Engineers.
• Develop batch and real‑time processing solutions using distributed computing technologies.
• Implement data governance, security, privacy and compliance controls.
• Optimize pipelines for performance, scalability, reliability and cost efficiency.
• Implement monitoring, logging, alerting and observability; troubleshoot failures and performance bottlenecks.
• Maintain data lineage, metadata, documentation and audit artifacts.
• Support data migration, modernization and cloud transformation initiatives.
• Collaborate with DevOps teams to automate deployments and CI/CD pipelines for data workloads.

Required profile

• Proven experience in Data Engineering, Big Data Engineering or related field.
• Strong hands‑on experience designing and maintaining ETL/ELT pipelines and enterprise data architectures.
• Advanced knowledge of SQL and data‑modeling principles.
• Solid programming experience with Python and/or Scala.
• Experience with relational and NoSQL databases.
• Familiarity with data lakes, data warehouses and large‑scale analytical platforms.

Required skills

• ETL/ELT pipeline development
• SQL
• Python
• Scala
• Data modeling
• Relational databases (e.g., PostgreSQL, Oracle)
• NoSQL databases (e.g., Cassandra, MongoDB)
• Data lakes and data warehouses
• Distributed computing frameworks (e.g., Apache Spark)
• Real‑time streaming platforms (e.g., Kafka)
• Data governance and security controls
• CI/CD and DevOps automation tools