PySpark Developer @ Link Group

3 weeks ago


Remote, Czech Republic Link Group Full time

Technical Skills:

  • 5+ years of experience in data engineering or big data development, with a strong focus on PySpark.
  • Proficiency in Python programming, with experience in libraries commonly used in data processing (e.g., Pandas, NumPy).
  • Strong understanding of Apache Spark concepts: Spark Core, Spark SQL, and Spark Streaming.
  • Experience with distributed data processing frameworks and working in cloud-based environments (e.g., AWS, Azure, GCP).
  • Solid knowledge of big data technologies like Hadoop, Hive, HDFS, Kafka, or Airflow.
  • Hands-on experience with relational and NoSQL databases (e.g., PostgreSQL, Cassandra).
  • Familiarity with CI/CD pipelines and version control (e.g., Git).

Soft Skills:

  • Strong analytical and problem-solving skills.
  • Ability to work collaboratively in a team and communicate technical concepts effectively.
  • Detail-oriented, with a commitment to delivering high-quality code.

Preferred Qualifications:

  • Experience with streaming data using Spark Streaming or Kafka.
  • Knowledge of machine learning workflows and integration with big data pipelines.
  • Understanding of containerization tools like Docker or orchestration with Kubernetes.
Job Description

We are seeking a highly skilled PySpark Developer with at least 5 years of experience in big data processing and analytics. The ideal candidate will design, implement, and optimize large-scale data processing pipelines, leveraging the capabilities of Apache Spark and Python.

,[Develop, test, and maintain PySpark-based ETL pipelines to process and analyze large datasets., Collaborate with data engineers, data scientists, and business stakeholders to understand data requirements and design optimal solutions., Optimize PySpark applications for performance and scalability in distributed computing environments., Work with Hadoop-based data platforms and integrate with other tools like Hive, HDFS, or Kafka., Ensure data quality and integrity through robust validation and monitoring practices., Debug and resolve issues in production and pre-production environments., Document technical solutions and best practices.] Requirements: Data engineering, Big Data, PySpark, Python, pandas, NumPy, Apache Spark, AWS, Azure, GCP, Hadoop, Hive, HDFS, Kafka, Airflow, PostgreSQL, Cassandra, CI/CD Pipelines, Git, Machine Learning, Docker, Kubernetes Tools: Agile, Scrum.

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