We are seeking a highly skilled Mediation Data Engineer to design, develop, and maintain data processing systems that collect, transform, and deliver usage data for billing and analytics platforms. In this role, you will focus on building robust data mediation pipelines that process large volumes of transactional data, ensuring accurate and real-time integration into billing systems. You will collaborate with teams across product, engineering, and finance to ensure smooth data flow, data quality, and compliance with industry standards. 

Responsibilities: Data Mediation Development: Design and implement mediation systems to collect, transform, and process raw usage data (e.g., call data records, messaging transactions) into standardized formats for billing and analytics platforms. ETL Pipeline Creation: Build and maintain efficient ETL processes to process high volumes of usage data, ensuring data accuracy, consistency, and readiness for billing calculations. Real-Time Event Processing: Develop and manage near real-time data pipelines using tools such as Kafka, Flink, or Spark Streaming, ensuring minimal latency between data ingestion and processing. Data Transformation: Implement data transformation rules to normalize and enrich data from multiple sources, preparing it for accurate billing, invoicing, and reporting. Automation and Efficiency: Automate mediation processes, minimizing manual intervention and improving the efficiency of data flows. Utilize orchestration tools like Apache Airflow or NiFi to manage workflows. Data Integrity and Quality: Ensure data quality by implementing checks, monitoring systems, and error-handling mechanisms to prevent inconsistencies, duplicates, or data loss. Collaboration with Stakeholders: Work closely with billing, product, and engineering teams to understand data requirements and ensure that mediation pipelines align with business needs. Compliance and Governance: Ensure that all mediation processes adhere to data governance, security, and regulatory requirements, particularly in relation to billing data.