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Telecom Network Data Performance Architect

Negotiable Salary

Qode

Florida, USA

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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).

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Florida, USA
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