Python & Spark Developer

As a Python & Spark Developer, you will be responsible for designing, developing, and maintaining scalable data-driven and event-driven applications. You will work on backend services using Python (Django, Flask) and integrate them with distributed data processing pipelines using PySpark. Your role will involve collaborating with cross-functional teams, guiding and mentoring junior resources, and ensuring smooth deployments through CI/CD pipelines using Jenkins and Docker. Key Responsibilities: - Design, develop, and maintain scalable data-driven and event-driven applications. - Develop backend services using Python (Django, Flask) and integrate them with distributed data processing pipelines using PySpark. - Work independently on assigned tasks and take complete ownership from design to deployment. - Collaborate with cross-functional teams for requirements gathering, design discussions, and delivery planning. - Guide and mentor junior resources in coding best practices and problem-solving. - Work with RDBMS and NoSQL databases to design and optimize storage solutions. - Integrate applications with messaging services like Kafka and MQ for event-driven architectures. - Ensure smooth deployments through CI/CD pipelines using Jenkins and Docker. Qualifications: - Bachelors or Masters degree in Computer Science, Engineering, or related field. - 4+ years of professional software development experience. - At least 2+ years of hands-on experience with PySpark for big data processing. - Strong backend development experience with Django and Flask. - Proven ability to work independently and take end-to-end ownership of tasks. - Strong problem-solving, communication, and mentoring skills. Core Skills & Technologies: - Languages: Python 3 - Frameworks: Apache Spark (PySpark), Django, Flask - Databases: PostgreSQL (RDBMS), Cassandra, MongoDB (NoSQL) - Messaging: Kafka, MQ - Architecture: Event-Driven, Data-Driven - CI/CD Tools: Jenkins, Docker - Monitoring tools: ELK, Prometheus, Grafana Good to Have: - Knowledge of Data Lake and Data Warehouse concepts. - Exposure to large-scale distributed systems. - Exposure to any of cloud provider [GCP, AWS, Azure] As a Python & Spark Developer, you will be responsible for designing, developing, and maintaining scalable data-driven and event-driven applications. You will work on backend services using Python (Django, Flask) and integrate them with distributed data processing pipelines using PySpark. Your role will involve collaborating with cross-functional teams, guiding and mentoring junior resources, and ensuring smooth deployments through CI/CD pipelines using Jenkins and Docker. Key Responsibilities: - Design, develop, and maintain scalable data-driven and event-driven applications. - Develop backend services using Python (Django, Flask) and integrate them with distributed data processing pipelines using PySpark. - Work independently on assigned tasks and take complete ownership from design to deployment. - Collaborate with cross-functional teams for requirements gathering, design discussions, and delivery planning. - Guide and mentor junior resources in coding best practices and problem-solving. - Work with RDBMS and NoSQL databases to design and optimize storage solutions. - Integrate applications with messaging services like Kafka and MQ for event-driven architectures. - Ensure smooth deployments through CI/CD pipelines using Jenkins and Docker. Qualifications: - Bachelors or Masters degree in Computer Science, Engineering, or related field. - 4+ years of professional software development experience. - At least 2+ years of hands-on experience with PySpark for big data processing. - Strong backend development experience with Django and Flask. - Proven ability to work independently and take end-to-end ownership of tasks. - Strong problem-solving, communication, and mentoring skills. Core Skills & Technologies: - Languages: Python 3 - Frameworks: Apache Spark (PySpark), Django, Flask - Databases: PostgreSQL (RDBMS), Cassandra, MongoDB (NoSQL) - Messaging: Kafka, MQ - Architecture: Event-Driven, Data-Driven - CI/CD Tools: Jenkins, Docker - Monitoring tools: ELK, Prometheus, Grafana Good to Have: - Knowledge of Data Lake and Data Warehouse concepts. - Exposure to large-scale distributed systems. - Exposure to any of cloud provider [GCP, AWS, Azure]