Data Integration & Data Quality Engineer; Google Cloud Platform

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Data Integration & Data Quality Engineer (Google Cloud Platform)

Mckinney, TX

6+ months

10+ Years

Data Integration & Data Quality Engineer to design and implement a scalable enterprise data architecture that consolidates customer data across Service Now, SAP, SAS, and Cattell-type source systems into Google Cloud Platform (Big Query). This role focuses on building robust ingestion pipelines, validating data quality before consolidation, and enabling trusted analytics through SQL-driven transformation layers and Power BI reporting.

• Architect end-to-end data pipelines to ingest customer and operational data from multiple enterprise platforms into Google Cloud Platform Big Query
• Design scalable ETL workflows using Airflow orchestration + SQL transformations
• Build data validation frameworks to detect anomalies, duplicates, schema drift, and integrity issues prior to consolidation
• Develop standardized data models for telecom customer entities (accounts, subscriptions, usage, billing, support interactions)
• Implement data quality rules, reconciliation logic, and monitoring dashboards
• Collaborate with enterprise architects and business teams to define canonical schemas and integration patterns
• Optimize query performance and storage design in Big Query
• Support downstream BI use cases using Power BI semantic models
• Document architecture, lineage, and governance rules for enterprise consumption

• Strong SQL expertise (advanced joins, window functions, optimization)
• Experience building ETL pipelines and orchestration using Apache Airflow
• Hands-on experience with Google Cloud Platform (Big Query, Cloud Storage, IAM, APIs)
• Experience integrating enterprise platforms (SAP, Service Now, SAS or similar systems)
• Strong understanding of data quality frameworks, validation rules, and reconciliation logic
• Knowledge of data modeling (dimensional + normalized models)
• Experience building data pipelines handling large customer datasets
• Ability to troubleshoot complex data inconsistencies across source systems

• Telecom domain experience (customer lifecycle, billing, provisioning, usage data)
• Experience designing enterprise master/customer data layers
• Familiarity with data governance, lineage, and audit frameworks
  
  Exposure to modern data architecture patterns (lakehouse, medallion layers, CDC pipelines)
• Experience enabling analytics/reporting in Power BI or similar BI tools

Data Quality, Data Integration, Google Cloud Platform, ETL, SQL, Big Query, Cloud Storage.