Senior Data Engineer Telecom OSS, ETL and Analytics

Telecom OSS & Domain Expertise Strong hands-on experience in Telecom OSS (Operations Support Systems). Experience working with Telecom Network Analytics. Understanding of Service Assurance systems. Experience in Fault Management systems. Knowledge of Performance Management systems. Experience handling telecom operational and network datasets. Data Engineering & ETL ETL Development. Data Engineering and Data Integration. Large-Scale Data Processing. Data Pipeline Development. Data Validation, Reconciliation, and Transformation. Performance Tuning and Optimization. Data Analytics Data Analytics and Reporting. Data-Driven Decision Making. Dashboarding and Operational Reporting. Analytics Solution Development. Programming & Development Python Development. SQL and Database Development. Database Performance Optimization. Shell Scripting. Unix/Linux Environments. Workflow Orchestration Apache Airflow. Workflow Automation. Job Scheduling and Monitoring. Experience: 5–9 Years Location: Hyderabad,Chennai,Bangalore Qualification: B.E./B.Tech in Computer Science, Information Technology, Data Science, Engineering, or equivalent RESPONSIBILITIES Design, develop, and maintain large-scale ETL pipelines and data integration frameworks for Telecom OSS systems. Develop scalable data engineering solutions for structured, semi-structured, and operational telecom datasets. Build and manage workflow orchestration pipelines using Apache Airflow. Develop data analytics, reporting, and dashboard solutions to support operational and business intelligence requirements. Collaborate with business stakeholders and technical teams to understand data requirements and deliver effective solutions. Implement data validation, reconciliation, transformation, and data quality processes. Optimize SQL queries, ETL jobs, and database performance for high-volume data workloads. Develop automation and analytics solutions using Python and Shell Scripting. Support Telecom Network Analytics, Service Assurance, Fault Management, and Performance Management initiatives. Participate in predictive analytics, anomaly detection, and AI/ML-driven use cases. Troubleshoot complex production issues and perform root cause analysis for data and application-related challenges. Contribute to cloud adoption, data modernization, and platform optimization initiatives. Machine Learning & Advanced Analytics Scikit-Learn TensorFlow PyTorch Predictive Analytics Models Machine Learning Solution Development Data Platforms & Architecture Data Lake Technologies Data Warehouse Solutions Big Data Ecosystems Modern Data Architectures Telecom Analytics Telecom Network Analytics