What are the responsibilities and job description for the GCP Data Performance architect ( Google Cloud) position at RELQ TECHNOLOGIES?
Job Titile : GCP Data Performance architect ( Google Cloud) Location : Remote Key Responsibilities:
Architect and implement logical, physical, and semantic data models for telecom network performance datasets including PM counters, CDRs, alarms, logs, probe data, and OSS KPIs.
Design time-series, geospatial, and hierarchical data models optimized for BigQuery and Dataflow pipelines.
Standardize telecom KPIs, KQIs, and service quality metrics into reusable schemas for assurance and optimization use cases.
Develop and maintain enterprise-wide data models aligned with TM Forum SID and other industry standards.
Collaborate with data engineering teams to translate models into efficient ingestion, transformation, and storage solutions on GCP.
Optimize BigQuery performance through strategic decisions on normalization, partitioning, clustering, and query tuning.
Define semantic layers for BI and analytics platforms (e.g., Looker, Looker Studio) to ensure consistent exposure of network KPIs.
Implement metadata management, lineage tracking, and cataloging using Dataplex for governed access to telecom datasets.
Support data scientists and ML engineers in designing feature stores and preparing model-ready datasets.
Required Skills & Experience
Telecom Domain Expertise:
In-depth understanding of network performance data across RAN, Core, Transport, and IP domains.
Experience modeling KPIs, QoS/QoE metrics, SON data, alarms, and service assurance datasets.
Familiarity with time-series, geospatial, and hierarchical data relationships in telecom environments.
Data Modeling & Architecture (GCP)
Proven experience in conceptual, logical, and physical data modeling for large-scale datasets.
Advanced proficiency in BigQuery optimization techniques including partitioning and clustering.
Hands-on experience with ER modeling tools (e.g., ERWin, Lucidchart, SQLDBM).
Expertise in semantic modeling for BI platforms such as Looker and Tableau.
Strong command of SQL (BigQuery dialect) and Python for data validation and transformation.
Cloud & Data Engineering Knowledge
Experience with Dataflow/Apache Beam for schema enforcement and pipeline development.
Familiarity with GCP services like Dataplex, Pub/Sub, and Cloud Storage for ingestion and data management.
Exposure to feature engineering and ML data preparation; integration with Vertex AI is a plus.
Preferred Qualifications
8 years of experience in data architecture/modeling, with at least 3 years focused on telecom data.
Strong background in OSS/BSS data models and TM Forum SID frameworks.
Google Cloud Professional Data Engineer or Architect certification.
Experience with 5G network data modeling including slicing, edge computing, and IoT analytics.
Architect and implement logical, physical, and semantic data models for telecom network performance datasets including PM counters, CDRs, alarms, logs, probe data, and OSS KPIs.
Design time-series, geospatial, and hierarchical data models optimized for BigQuery and Dataflow pipelines.
Standardize telecom KPIs, KQIs, and service quality metrics into reusable schemas for assurance and optimization use cases.
Develop and maintain enterprise-wide data models aligned with TM Forum SID and other industry standards.
Collaborate with data engineering teams to translate models into efficient ingestion, transformation, and storage solutions on GCP.
Optimize BigQuery performance through strategic decisions on normalization, partitioning, clustering, and query tuning.
Define semantic layers for BI and analytics platforms (e.g., Looker, Looker Studio) to ensure consistent exposure of network KPIs.
Implement metadata management, lineage tracking, and cataloging using Dataplex for governed access to telecom datasets.
Support data scientists and ML engineers in designing feature stores and preparing model-ready datasets.
Required Skills & Experience
Telecom Domain Expertise:
In-depth understanding of network performance data across RAN, Core, Transport, and IP domains.
Experience modeling KPIs, QoS/QoE metrics, SON data, alarms, and service assurance datasets.
Familiarity with time-series, geospatial, and hierarchical data relationships in telecom environments.
Data Modeling & Architecture (GCP)
Proven experience in conceptual, logical, and physical data modeling for large-scale datasets.
Advanced proficiency in BigQuery optimization techniques including partitioning and clustering.
Hands-on experience with ER modeling tools (e.g., ERWin, Lucidchart, SQLDBM).
Expertise in semantic modeling for BI platforms such as Looker and Tableau.
Strong command of SQL (BigQuery dialect) and Python for data validation and transformation.
Cloud & Data Engineering Knowledge
Experience with Dataflow/Apache Beam for schema enforcement and pipeline development.
Familiarity with GCP services like Dataplex, Pub/Sub, and Cloud Storage for ingestion and data management.
Exposure to feature engineering and ML data preparation; integration with Vertex AI is a plus.
Preferred Qualifications
8 years of experience in data architecture/modeling, with at least 3 years focused on telecom data.
Strong background in OSS/BSS data models and TM Forum SID frameworks.
Google Cloud Professional Data Engineer or Architect certification.
Experience with 5G network data modeling including slicing, edge computing, and IoT analytics.
