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