Senior Data Engineer -Microsoft Fabric / Azure (Ref: 5847)

Estrada Consultingorporated•Sacramento, CA

3d•Remote

About The Position

The Senior Data Engineer will support the modernization, integration and expansion of an enterprise Data Warehouse and Business Intelligence environment. The role will focus on building and maintaining reliable data pipelines, integrating new data sources, developing relational and semantic data models, and supporting migration and modernization initiatives using Microsoft Fabric and Azure technologies.

Requirements

Microsoft Fabric.
Azure data services.
SQL / MS SQL Server.
ETL/ELT and enterprise data pipeline development.
Relational data modeling.
Semantic data modeling.
Data integration across multiple source types.
Data quality and reconciliation.
Pipeline monitoring, troubleshooting and production support.
PostgreSQL.
Power BI data integration/modeling.
Customer or participant data modeling.
Identity resolution / master or unified customer data concepts.
Metadata management and data lineage.
Data governance.
Cloud migration / modernization.
Predictive-analytics data preparation.
APIs and SaaS data integration.
POS/transactional system integration.

Responsibilities

Design, develop, enhance and maintain data pipelines integrating on-premises, SaaS, cloud, file-based and transactional/POS data sources.
Assess and transition applicable relational and semantic data models to Microsoft Fabric.
Integrate new data sources with existing enterprise data models while maintaining existing production pipelines.
Design and develop curated data models supporting analytics, reporting and customer/participant analysis.
Support development of customer-oriented data models involving identity resolution, customer attributes, household relationships, transactional data and segmentation.
Prepare trusted, analytics-ready datasets for Power BI and other reporting/analytics uses.
Work with BI resources to support advanced metrics, trend analysis, forecasting and predictive-analytics requirements.
Implement and maintain data quality, validation, reconciliation and transformation rules.
Support data governance through metadata management, lineage, taxonomy standards and normalized reference data.
Implement production-grade pipeline controls including error handling, automated retries, logging, monitoring, data-freshness checks and operational support.
Troubleshoot pipeline failures, data-quality issues and integration problems.
Support secure data access using appropriate RBAC, least-privilege and data-protection practices.
Coordinate with technical teams, business stakeholders and third-party system vendors.