GCP Data Performance architect ( Google Cloud)
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Monthly Salary:
Posted on:
30+ days ago
Vacancies:
1 Vacancy
Job Summary
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.
Key Skills
- Fund Management
- Drafting
- End User Support
- Infrastructure
- Airlines
- Catia
