Data Engineer — Hybrid, Scalable Pipelines & AI Tools

Engg; Kraków; On-site; PLN 150,000 - 210,000; Full time

Job summary

Cisco's Webex Engineering Group is hiring a Data Engineer to design, implement, and optimize scalable data pipelines for streaming and batch processing. You'll work with senior engineers to build robust solutions using Spark, Flink, Pinot, and Iceberg, enabling data-driven insights for partners.

Join a hybrid team based in Bratislava, contributing to the production tech stack and improving observability and reliability across data systems.

Qualifications

• Bachelor's degree in Computer Science, Engineering, or equivalent practical experience.
• 3+ years of professional data engineering or software engineering experience.
• Foundational knowledge of data processing frameworks (e.g., Spark, Flink) and distributed systems.
• Willingness to learn new technologies quickly.

Responsibilities

• Design, implement, and maintain data pipelines for streaming and batch processing.
• Develop and optimize data workflows using Pinot, Iceberg, Flink, and Spark.
• Participate in the full software development lifecycle, including design reviews and testing.
• Improve data pipeline performance, reliability, and observability.
• Collaborate with software engineers, product management, and data teams.
• Learn from production issues via postmortems and continuous learning.

Skills

Data engineering experience

Spark / Flink knowledge

Programming: Java / Python / Scala

Distributed systems understanding

Education

Bachelor's degree in Computer Science, Engineering, or equivalent

Tools

Apache Pinot

Apache Iceberg

Flink

Spark

Git

CI/CD

Job description

Cisco's Webex Engineering Group is hiring a Data Engineer to design, implement, and optimize scalable data pipelines for streaming and batch processing. You'll work with senior engineers to build robust solutions using Spark, Flink, Pinot, and Iceberg, enabling data-driven insights for partners.

Join a hybrid team based in Bratislava, contributing to the production tech stack and improving observability and reliability across data systems.