Python Pyspark

As a Python Pyspark developer, you'll be responsible for writing and optimizing code in Python and PySpark, testing applications using frameworks like Pytest, collaborating with AWS services, designing scalable data applications, and implementing CI/CD pipelines. Your day-to-day work will focus on enhancing our data processing capabilities.

To qualify as a Python Pyspark developer, you should have extensive experience in Python programming and PySpark, familiarity with AWS cloud platforms and data pipeline tools, and a solid understanding of continuous integration and deployment processes. A background in frameworks like Django or Flask is also beneficial.

Ideal candidates for the Python Pyspark developer position should be proficient in using tools such as Pytest for testing, Apache Airflow or Kafka for data pipelines, and Jenkins for automation. Additionally, experience with AWS tools like S3 and Databricks is highly desirable.

This position is flexible! While it is based in Wilmington, DE, we welcome non-local applicants who are interested in remote work opportunities. We value talent from diverse locations.

Success as a Python Pyspark developer requires strong technical skills, excellent problem-solving abilities, and the capacity to work collaboratively within a team. Staying current with the latest technology trends in data processing and continuous improvement will also help you thrive in this role.

When debugging in Python, I follow a systematic approach: first, I use print statements to trace variable values, then I utilize built-in debugging tools like pdb. Additionally, leveraging frameworks such as Pytest allows me to create clear test cases to identify faults more effectively.

Absolutely! PySpark operates on distributed datasets with in-memory computation, making it much faster than traditional MapReduce, which writes intermediate data to disk. This in-memory processing allows PySpark to handle large-scale data analytics efficiently.

I have extensive experience with AWS, particularly with services such as S3 for storage and using Databricks for data processing. I also harness AWS Data Lake Storage for scalable data storage solutions, ensuring data accessibility across projects.

Scaling applications in PySpark involves optimizing code, leveraging caching for data, and using partitioning strategies to manage large datasets. I also utilize Spark's cluster management features to ensure efficient resource allocation as demand grows.

I have worked extensively with CI/CD tools like Jenkins to automate testing and deployment processes. Implementing CI/CD practices enables rapid iterations and ensures that our applications maintain high quality while allowing for speedy delivery.

I prioritize writing modular code, adhering to PEP 8 style guidelines for Python, and incorporating thorough documentation. Regular code reviews and using version control contribute to maintaining a clean codebase that is easy for others to understand.

Ensuring data integrity starts with validating data inputs, followed by implementing checks throughout the data pipeline. I use testing frameworks to establish integrity measures and monitor all data flows to identify discrepancies before they become issues.

I have worked with several data pipeline tools like Apache Airflow to orchestrate data workflows and Kafka for real-time data streaming. Using these tools, I can build robust data ecosystems that are scalable and efficient.

In a previous role, I encountered performance issues due to inefficient data processing. By profiling the PySpark jobs and optimizing the data partitioning strategy, I reduced processing time by 50%, which improved overall application performance significantly.

I actively follow industry blogs, participate in online forums, and attend webinars and conferences related to data science and analytics. Continuous learning is crucial in staying abreast of advancements and incorporating them into my projects.