Lead Data Engineer - Databricks & Microsoft Fabric

Location: Remote (Preference for candidates located in New England)

Engagement: Contract or Contract-to-Hire

Overview

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.

Responsibilities

• 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

Preferred

• Experience supporting AI or machine learning workloads

• Experience working in enterprise data environments

• Familiarity with CI/CD and data platform automation