Lead Data Engineer – Databricks, Microsoft Fabric

Job not on LinkedIn

🕒 March 10

⚓ Rhode Island – Remote

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⏳ Contract/Temporary

🟠 Senior

🚰 Data Engineer

👻 Ghost score 45%

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Logo of Spyglass MTG, LLC.

Spyglass MTG, LLC.

51 - 200 employees

Founded 2018

💼 Consulting

🤖 Artificial Intelligence

🏢 Enterprise

Consulting • Artificial Intelligence • Enterprise

Spyglass MTG, LLC is a certified Microsoft-focused consulting firm that helps organizations select, architect, implement, migrate to, and manage Microsoft technologies. Their offerings emphasize AI (Agentic AI, AI Navigator, Copilot), data platforms (including Microsoft Fabric LOOM), data governance, security and identity/access management, cloud solutions, infrastructure operations, app modernization, and migrations, plus Microsoft licensing services. Spyglass serves enterprise clients across sectors such as higher education, financial services, healthcare, legal, manufacturing, and oil & gas, and highlights rapid AI deployment, repeat customers, and Copilot/Power Platform enablement.

📋 Description

• Design and implement modern data platforms using Databricks and Microsoft Fabric • Build and maintain scalable data pipelines and ingestion frameworks • Develop and optimize Spark and PySpark workloads within Databricks • Implement lakehouse and data warehouse architectures • Design and manage data models supporting analytics and reporting • Collaborate with data scientists and analytics teams to support AI and advanced analytics initiatives • Implement data quality, governance, and security best practices • Provide technical leadership and mentorship to data engineering team members • Optimize performance, reliability, and scalability of data platforms

🎯 Requirements

• 7+ years of experience in data engineering or data platform development • Strong hands-on experience with Databricks and Apache Spark • Experience implementing Microsoft Fabric data solutions • Proficiency in Python and PySpark • Experience working with Azure data services and cloud-based data platforms • Experience designing modern lakehouse or data warehouse architectures • Strong understanding of data pipeline design, ETL/ELT processes, and data modeling • Experience supporting AI or machine learning workloads • Experience working in enterprise data environments • Familiarity with CI/CD and data platform automation

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