Negotiable Salary
Qode
Florida, USA
Role Overview We are seeking a Telecom Network Data Performance Architect with deep expertise in data modeling and architecture on Google Cloud Platform (GCP). The role focuses on building robust, scalable, and domain-driven data models for telecom network performance management, enabling analytics, AI/ML, and automation use cases across network operations and customer experience. Key Responsibilities · Design and implement logical, physical, and semantic data models for telecom network performance datasets (PM counters, CDRs, alarms, logs, probe data, OSS KPIs). · Develop time-series, geospatial, and hierarchical data models optimized for BigQuery and Dataflow pipelines. · Standardize telecom KPIs, KQIs, and service quality metrics into reusable data schemas for assurance and optimization. · Build and maintain enterprise data models aligned with TM Forum SID / industry standards. · Collaborate with data engineers to translate models into efficient ingestion, transformation, and storage patterns on GCP. · Ensure data normalization vs denormalization trade-offs, partitioning and clustering strategies, and performance tuning in BigQuery. · Define semantic layers for BI and analytics (Looker/Looker Studio) to expose network KPIs consistently. · Implement metadata, lineage, and cataloging using Dataplex for governed access to telecom datasets. · Guide data scientists and AI/ML engineers in feature store design and model-ready data sets. Required Skills & Experience Telecom Domain Modeling: · Strong understanding of network performance management data (RAN, Core, Transport, IP). · Experience in modeling KPIs, QoS/QoE metrics, SON, alarms, and service assurance data. · Familiarity with time-series, geospatial, and hierarchical relationships in network data. Data Modeling & Architecture (GCP): · Expertise in conceptual, logical, and physical data modeling for large-scale datasets. · Advanced knowledge of BigQuery partitioning, clustering, and optimization. · Hands-on with ER modeling tools (e.g., ERWin, Lucidchart, SQLDBM). · Experience with semantic modeling for BI platforms (Looker, Tableau, etc.). · Proficiency in SQL (BigQuery dialect) and Python for data validation. Cloud & Data Engineering Knowledge: · Exposure to Dataflow/Apache Beam for schema enforcement in pipelines. · Knowledge of Dataplex, Pub/Sub, Cloud Storage for modeling ingestion pipelines. · Experience in feature engineering & ML data model preparation (Vertex AI integration is a plus). Preferred Qualifications · 8+ years in data architecture / modeling, with at least 3+ in telecom data. · Strong background in OSS/BSS data models and TM Forum SID frameworks. · Certification: Google Cloud Professional Data Engineer / Architect. · Exposure to 5G network data modeling (slicing, edge, IoT analytics).