Data Integration & Data Quality Engineer (GCP | Telecom | ETL/SQL)

Data Integration & Data Quality Engineer to design and implement a scalable enterprise data architecture that consolidates customer data across ServiceNow, SAP, SAS, and Cattell-type source systems into Google Cloud Platform (BigQuery). 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 GCP BigQuery 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 BigQuery Support downstream BI use cases using Power BI semantic models Document architecture, lineage, and governance rules for enterprise consumption Skills Strong SQL expertise (advanced joins, window functions, optimization) Experience building ETL pipelines and orchestration using Apache Airflow Hands-on experience with GCP (BigQuery, Cloud Storage, IAM, APIs) Experience integrating enterprise platforms (SAP, ServiceNow, 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 Preferred 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