Lead Engineer Bigdata - PySpark

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Overview

We are seeking a highly skilled and experienced Senior Bigdata/PySpark Engineer to join our dynamic Big Data Analytics team. The ideal candidate will have a strong background in Python programming and extensive experience with Apache Spark, particularly PySpark, for large-scale data processing and analytics. This role involves designing, developing, and optimizing robust and scalable data pipelines, working with vast datasets, and contributing to the architecture of our Big Data solutions.

Responsibilities:

• Design, develop, and maintain efficient, scalable, and reliable data pipelines using PySpark.
• Implement complex data transformations, aggregations, and data quality checks on large datasets.
• Collaborate with multiple stakeholders (technology and business) to understand data requirements and translate them into technical specifications.
• Optimize PySpark jobs for performance, efficiency, and cost-effectiveness.
• Develop and maintain documentation for data pipelines, data models, and data processing logic.
• Participate in code reviews, ensuring code quality, best practices, and adherence to established standards.
• Troubleshoot and resolve issues in existing data pipelines and data processing jobs.
• Stay up-to-date with the latest advancements in PySpark, Apache Spark, and the broader Big Data ecosystem.
• Mentor junior developers and contribute to the continuous improvement of the team's technical capabilities and processes.

Required Qualifications:

• 8-12 years of relevant experience
• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field.
• 5+ years of professional experience in software development with a focus on Big Data technologies.
• 5+ years of hands-on experience specifically with PySpark for large-scale data processing.
• Strong proficiency in Python programming, including object-oriented design and data manipulation libraries (e.g., Pandas, NumPy).
• In-depth understanding of Apache Spark architecture, including Spark Core, Spark SQL, Spark Streaming, and DataFrame API.
• Experience with various data storage technologies such as HDFS, S3, Azure Blob Storage, or similar distributed file systems.
• Solid understanding of relational databases and SQL.
• Experience with version control systems (e.g., Git).
• Excellent problem-solving, analytical, and communication skills.

Preferred Qualifications:

• Experience with cloud platforms (AWS, Azure, GCP) and their Big Data services (e.g., EMR, Databricks, Glue, Azure Synapse, Google Dataproc).
• Familiarity with workflow orchestration tools (e.g., Apache Airflow, Luigi).
• Experience with streaming data processing (e.g., Kafka, Spark Streaming).
• Knowledge of data warehousing concepts and data modeling techniques.
• Experience with containerization technologies (e.g., Docker, Kubernetes).
• Understanding of data governance, data security, and compliance best practices.

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