Senior Data Engineering Professional

1 week ago

Islamabad, Islamabad, Pakistan beBeeDataEngineering Full time 2,000,000 - 2,500,000

We are looking for an experienced data engineering professional to lead our team in designing and implementing robust, scalable, and efficient data pipelines using streaming frameworks such as Kafka, Apache Flink, and Spark Structured Streaming.

The ideal candidate will have a strong background in real-time and batch data processing, containerization, and cloud-based data orchestration.

Key Responsibilities:
  1. Design and maintain data pipelines that are resilient, scalable, and highly performant.
  2. Develop and implement batch processing workflows with Apache Spark (PySpark).
  3. Orchestrate and schedule data workflows using tools like Apache Airflow and Azure Data Factory.
  4. Containerize applications using Docker, manage deployments with Helm, and run them on Kubernetes.
  5. Implement modern storage solutions using open formats such as Parquet, Delta Lake, and Apache Iceberg.
  6. Build high-performance analytics engines using tools like Trino or Presto.
  7. Collaborate with DevOps to manage infrastructure with Terraform and integrate with CI/CD pipelines via Azure DevOps.
  8. Ensure data quality and consistency using tools like Great Expectations.
  9. Write modular, well-tested, and maintainable Python and SQL code.

The successful candidate will be able to work collaboratively with cross-functional teams to drive business outcomes through data-driven insights.

Requirements:
  1. Advanced Python programming with a focus on modular and testable code.
  2. Strong knowledge of SQL and experience working with large-scale datasets.
  3. Hands-on experience with at least one major cloud platform.
  4. Solid experience with real-time data processing.
  5. Expertise in Apache Spark (PySpark) for batch processing.
  6. Experience implementing lakehouse architectures and working with columnar storage.
  7. Proficient in using data orchestration tools.
  8. Experience building APIs to expose large datasets.
  9. Solid experience with containerization and orchestration.
  10. Familiarity with data lake open formats.
  11. Basic experience with infrastructure provisioning.
  12. Practical experience with data quality frameworks.
  13. Comfortable working in agile development teams.
  14. Proven ability in debugging and performance tuning of data jobs.

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