Data Engineer

Role Summary
The Data Engineer is responsible for consolidating and maintaining the organization’s data infrastructure, ensuring data accuracy, consistency, and reliability. This role involves designing, building, and managing scalable ETL processes, establishing enterprise data warehouse architecture, and supporting data feeds for internal and external partners. Collaboration with application developers and business stakeholders is essential to transform raw data into trustworthy information supporting reporting and analytics needs.

Responsibilities

• Support the implementation and integration of Microsoft Fabric as the organization’s modern data platform, including migration of existing data sources.
• Design, develop, and monitor scalable ETL pipelines using Azure Data Factory, Fabric Data Factory, Dataflows Gen2, notebooks, and Spark-based processing.
• Develop and maintain enterprise data warehouse architecture to support reliable reporting and analytical functions.
• Design and optimize data models, including dimensional models, star schemas, and semantic models for performant analytics.
• Manage data feeds to and from internal and external partners, including contract oversight, format specifications, and monitoring feed health.
• Enforce data quality standards through validation rules, automated testing, reconciliation, and documentation of data lineage.
• Optimize data workflows and database performance through query tuning, resource management, and pipeline efficiency improvements.
• Automate data infrastructure operations using DataOps practices, enhancing reliability and reducing manual intervention.
• Establish best practices for data engineering, including coding standards, version control, CI/CD, and data governance.
• Collaborate with IT, security, and compliance teams to ensure ongoing adherence to privacy and security standards, including handling of PHI and PII data.

Qualifications

• Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or relevant field, or equivalent practical experience.
• 5+ years of experience in data engineering, including designing and operating production ETL pipelines.
• Hands-on experience with Microsoft Fabric, including building and managing Data Factory pipelines, Lakehouse or Warehouse, and notebooks.
• Strong familiarity with the broader Azure data stack, including Azure SQL, Azure Data Factory, Synapse Analytics, and Azure Data Lake.
• Proficiency in SQL, Python, and/or PySpark for data transformation and pipeline development.
• Practical experience implementing and maintaining enterprise data warehouse architecture.
• Knowledge of dimensional data modeling (star/snowflake schemas) and building semantic models for reporting.
• Experience managing data feeds, SLAs, and monitoring/reporting on their reliability.
• Demonstrated ability to implement data quality frameworks, validation, testing, and reconciliation processes.
• Strong understanding of data governance, security, and access control practices in a cloud environment.
• Excellent communication skills with the ability to collaborate across technical and non-technical teams.

Publishing Pay Range: $62.00 - $74.00 hourly
This is a fully remote role and can be performed from any approved location within the United States.