Lead Data Engineer - Databricks & Microsoft Fabric

Location: Remote (Preference for candidates located in New England)
Engagement: Contract or Contract-to-Hire

Databricks & Microsoft Fabric

Spyglass MTG is seeking a highly skilled Lead Data Engineer with strong expertise in Databricks and Microsoft Fabric to help design, build, and scale modern data platforms in Azure.

This role is both strategic and hands-on. The ideal candidate will lead the development of enterprise data solutions while actively building scalable pipelines, lakehouse architectures, and data platforms that support analytics, reporting, and AI initiatives.

The Lead Data Engineer will work closely with data architects, analysts, and business stakeholders to deliver high-performance data solutions that enable organizations to leverage their data more effectively.

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

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

Preferred

Experience supporting AI or machine learning workloads

Experience working in enterprise data environments

Familiarity with CI/CD and data platform automation