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Data Engineer

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MidFull-time·Umowa o staż
#440712·Dodano 5 dni temu·0
Źródło: Cisco
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Tech Stack / Keywords

Apache PinotApache IcebergFlinkSparkJavaPythonScalaGitCI/CDAI coding tools

Firma i stanowisko

Cisco's Webex Engineering Group builds collaboration technology powering products like Slido, Webex Meetings, Webex Calling, and Webex Contact Center. The team is based in Bratislava and operates a hybrid work model.

Wymagania

  • Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience.
  • 3+ years of professional data engineering or software engineering experience (or equivalent internship/project experience).
  • Foundational knowledge of data processing frameworks (e.g., Spark, Flink) and/or distributed systems.
  • Solid understanding of software development and architecture concepts with a willingness to learn new technologies quickly.

Preferred Qualifications:

  • Familiarity with Apache Pinot, Apache Iceberg, Flink, or Spark.
  • Familiarity with Java, Python, or Scala.
  • Familiarity with Git, CI/CD pipelines, and cloud environments.
  • Familiarity with AI coding tools (e.g., Codex, Cursor, Copilot).
  • Eagerness to debug and resolve production data issues.
  • Ability to apply creative skills to complex data problems.
  • Flexible, thrives in diverse teams, and communicates clearly.
  • Strong attention to detail and commitment to continuous learning and quality.

Obowiązki

  • Design, implement, and maintain data pipelines for streaming and batch processing with guidance from senior team members.
  • Develop and optimize data workflows using Apache Pinot, Apache Iceberg, Flink, and Spark.
  • Participate in design discussions, code reviews, and testing.
  • Improve data pipeline performance, reliability, and observability by resolving technical issues.
  • Leverage AI coding tools (e.g., Copilot, Cursor) to accelerate productivity and learning.
  • Collaborate with software engineers, product management, and data teams.
  • Participate in incident resolution and learn from production issues through postmortems.
  • Commit to continuous learning, data quality, and operational excellence.
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