Senior Python Engineer for Data Retraining

Are you prepared to take your Python engineering expertise to the next level and transition into the dynamic world of Big Data? EPAM provides a unique chance to secure a role after passing a single technical interview, while acquiring Big Data skills without changing your title or compensation. This focused 8-week program is tailored for Python engineers making the shift to Big Data Engineering. The curriculum encompasses three distinct phases: theoretical coursework delivered by industry professionals, practical tasks or hands-on projects, and a well-rounded knowledge assessment accompanied by feedback. Key topics include data management, distributed systems, Spark, Kafka, NoSQL databases, and cloud-native services, ensuring a solid grounding in modern data platforms. The training is fully remote, enabling participants to learn from the comfort of their own space. Responsibilities Develop a strong foundation in Big Data by exploring core concepts, Hadoop infrastructure, real-world applications, data characteristics, and deployment trends Build familiarity with DevOps practices, including continuous integration and continuous deployment (CI/CD), to enhance software development and operational workflows Acquire essential data modeling techniques critical for managing and interpreting complex data structures in engineering and architectural roles Expand knowledge of Apache Spark by understanding its architecture, components, and functionalities such as Spark SQL, Spark ML, and Spark Streaming Gain expertise in Kafka fundamentals and tools like Kafka Connect and Kafka Streams for managing real-time data feeds and performing stream processing Explore Elastic Stack for real-time data analysis and visualization while understanding NoSQL databases to manage diverse data types Deepen understanding of data flow and pipelining with tools like NiFi and Streamsets to improve data collection, flow, and processing capabilities Learn orchestration and workflow management with tools such as Airflow and Jenkins to coordinate complex processes effectively Requirements 4+ years of production experience in IT Proficiency in Python, SQL, and cloud platforms (AWS, GCP, Azure) Background in tools such as Databricks, Spark, Docker, Kubernetes is a plus Familiarity with AI, LLM English proficiency at B2+ or higher