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.
