PySpark

Key Responsibilities:

PySpark Development:

• Design, implement, and optimize

  PySpark

  solutions for large-scale data processing and analysis.
• Develop

  data pipelines

  using

  Spark

  to handle data transformations, aggregations, and other complex operations efficiently.
• Write and optimize

  Spark SQL queries

  for big data analytics and reporting.
• Handle data extraction, transformation, and loading (ETL) processes from various sources into a unified data warehouse or data lake.

Data Pipeline Design & Optimization:

• Build and maintain

  ETL pipelines

  using

  PySpark

  , ensuring high scalability and performance.
• Implement

  batch and streaming processing

  to handle both real-time and historical data.
• Optimize the performance of PySpark applications by applying best practices and techniques such as

  partitioning

  ,

  caching

  , and

  broadcast joins

  .

Data Storage & Management:

• Work with large datasets and integrate them into storage solutions such as

  HDFS

  ,

  S3

  ,

  Azure Blob Storage

  , or

  Google Cloud Storage

  .
• Ensure efficient data storage, access, and retrieval through Spark and other tools (e.g.,

  Parquet

  ,

  ORC

  ).
• Maintain data quality, consistency, and integrity throughout the pipeline lifecycle.

Cloud Platforms & Big Data Frameworks:

• Deploy Spark-based applications on cloud platforms such as

  AWS (Amazon EMR)

  ,

  Azure HDInsight

  , or

  Google Dataproc

  .
• Work with cloud-native services such as

  AWS Lambda

  ,

  S3

  ,

  Google Cloud Storage

  , and

  Azure Data Lake

  to handle and process big data.
• Leverage cloud data processing tools and frameworks to scale and optimize the PySpark jobs.

Collaboration & Integration:

• Collaborate with cross-functional teams (data scientists, analysts, product managers) to understand business requirements and develop appropriate data solutions.
• Integrate data from multiple sources and platforms (e.g., databases, external APIs, flat files) into a unified system.
• Provide support for downstream applications and data consumers by ensuring timely and accurate delivery of data.

Performance Tuning & Troubleshooting:

• Identify bottlenecks and optimize Spark jobs to improve performance.
• Conduct performance tuning of both the

  cluster

  and individual

  Spark jobs

  , leveraging Spark's in-built tools for monitoring.
• Troubleshoot and resolve issues related to data processing, application failures, and cluster resource utilization.

Documentation & Reporting:

• Maintain clear and comprehensive documentation of data pipelines, architectures, and processes.
• Create technical documentation to guide future enhancements and troubleshooting.
• Provide regular updates on the status of ongoing projects and data processing tasks.

Continuous Improvement:

• Stay up to date with the latest trends, technologies, and best practices in big data processing and PySpark.
• Contribute to improving development processes, testing strategies, and code quality.
• Share knowledge and provide mentoring to junior team members on PySpark best practices.

Required Qualifications:

• 2-4 years

  of professional experience working with

  PySpark

  and big data technologies.
• Strong expertise in

  Python

  programming with a focus on data processing and manipulation.
• Hands-on experience with

  Apache Spark

  , particularly with PySpark for distributed computing.
• Proficiency in

  Spark SQL

  for data querying and transformation.
• Familiarity with cloud platforms like

  AWS

  ,

  Azure

  , or

  Google Cloud

  , and experience with

  cloud-native big data tools

  .
• Knowledge of

  ETL

  processes and tools.
• Experience with data storage technologies like

  HDFS

  ,

  S3

  , or

  Google Cloud Storage

  .
• Knowledge of data formats such as

  Parquet

  ,

  ORC

  ,

  Avro

  , or

  JSON

  .
• Experience with

  distributed computing

  and

  cluster management

  .
• Familiarity with

  Linux/Unix

  and command-line operations.
• Strong problem-solving skills and ability to troubleshoot data processing issues.