Job Details

ROLE: Java Tech lead

Location: Phoenix, Raleigh, Remote
Duration: 6 months

Experience requested: 8 to 10 Yrs

Description:
• Strong NiFi Expertise: Deep understanding of Apache NiFi architecture, processors, controllers, and data flow management capabilities.
• Programming Languages: Proficiency in Java, Spring boot & API Microservices.
• Database and SQL: Familiarity with databases, SQL, and data manipulation techniques.
• Big Data Technologies: Kafka ( Event driven & streaming architecture)
• Apachae Airflow - Workflow orchestration"

Design and Implementation:
o Creating and implementing data flow solutions using NiFi, including defining data sources, destinations, and transformation processes.
• Ingestion and Transformation:
o Managing data ingestion from various sources (e.g., files, databases, APIs) and transforming data into desired formats for downstream consumption.
• Routing and Enrichment:
o Configuring NiFi to route data to appropriate destinations and enriching data with additional information (e.g., timestamps, location).
• Scalability and Performance:
o Ensuring data flows are scalable and performant to handle varying data volumes and velocity.
• NiFi Cluster Management:
o Maintaining and improving NiFi clusters, including configuration, optimization, and troubleshooting.
• Microservices Integration:
o Developing and integrating microservices to support data conditioning, format validation, and transformation processes.
• Troubleshooting and Issue Resolution:
o Identifying and resolving issues related to data flows, NiFi configurations, and data integration processes
• Airflow Expertise:
o Possessing deep knowledge of Airflow's architecture, including schedulers, executors (Celery, Kubernetes), and plugin development.
• Workflow Design and Development:
o Designing and developing complex, modular, and reusable DAGs (Directed Acyclic Graphs) to automate data pipelines.
• Performance Optimization:
o Identifying and addressing performance bottlenecks in Airflow environments and implementing best practices for orchestration and scheduling