PySpark Developer Career

Functional Skills

Functional Skills are essential skills that help individuals perform tasks effectively in everyday life, work, and education.

Spark SQL

DataFrames API

PySpark RDDs

Python Programming

Data Engineering Principles

Cloud Computing (AWS, Azure, GCP)

Spark Performance Tuning

Big Data Technologies (Hadoop, Hive)

Version Control (Git)

Data Serialization Formats

Real-time Data Processing

Testing and Debugging

Soft Skills

Communication, teamwork, and critical thinking to ensure seamless project execution.

Technical Skills

Technical Skills are specialized abilities and knowledge needed to perform specific tasks or functions.

Apache Spark

Python Programming

PySpark API

SQL

Data Warehousing Concepts

Cloud Computing (AWS, Azure, GCP)

Data Analysis and Visualization

Linux and Shell Scripting

Version Control (Git)

DevOps Practices

Data Security

Real-time Data Processing

Duties and Responsiblities

Task

95%

Develop scalable data pipelines using PySpark for data ingestion, processing, and storage in cloud environments like AWS or Azure.

92%

Write and optimize PySpark code for large-scale data processing, ensuring performance and efficiency.

90%

Design and implement data models and schemas optimized for PySpark processing and analytics.

88%

Collaborate with data scientists and analysts to understand data requirements and translate them into PySpark solutions.

85%

Implement data quality checks and validation processes within PySpark pipelines to ensure data accuracy.

82%

Monitor and troubleshoot PySpark jobs, identifying and resolving performance bottlenecks and errors.

80%

Develop and maintain documentation for PySpark code, data pipelines, and data models.

78%

Implement security measures and access controls for data stored and processed using PySpark.

75%

Stay up-to-date with the latest PySpark features, best practices, and industry trends.

72%

Participate in code reviews and contribute to the improvement of PySpark development standards.

70%

Automate PySpark job scheduling and deployment using tools like Apache Airflow or similar orchestration platforms.

Work Activities

95%

Fine-tune Spark jobs for optimal performance, addressing issues like data skew and resource allocation.

92%

Design and implement robust data pipelines using PySpark for efficient data ingestion and processing.

90%

Automate ETL processes with PySpark, ensuring data quality and timely delivery for analytics.

88%

Perform large-scale data analysis using PySpark to extract insights and support business decisions.

85%

Manage and monitor Spark clusters, ensuring high availability and optimal resource utilization.

82%

Conduct code reviews and implement unit tests to maintain code quality and reliability in PySpark projects.

80%

Collaborate with data scientists and engineers, documenting PySpark workflows and best practices.

78%

Ingest data from various sources into the Spark ecosystem using PySpark, handling different data formats.

75%

Monitor the performance of PySpark applications and identify areas for improvement.

72%

Implement security measures for data processing in PySpark, ensuring compliance with data governance policies.

Detailed Work Profile

Interests