Software Engineering Manager; Data Engineering

Build the future, spark innovation and align your career with purpose.

McKinstry is innovating the waste and climate harm out of the built environment and creating lasting impact. Together, we’re building a thriving planet.

Buildings are a leading contributor to the climate crisis, generating nearly 40% of total global energy‑related carbon emissions. We’re making a lasting impact on our industry and within our communities by addressing the climate, affordability and equity crises through:

• renewables and energy services
• engineering and design
• construction and facility services

To get where we’re going, we need big thinkers, problem solvers and collaborative mindsets. Does that sound like you?

McKinstry is building a modern, scalable data platform to power analytics, reporting, and AI‑driven decision‑making—and we're looking for a Software Engineering Manager (Data Engineering) to lead the team at the center of that transformation.

In this role, you'll manage and develop a team of data engineers building and operating McKinstry's Azure Data Lakehouse. You'll guide the evolution from Azure Synapse Analytics to Microsoft Fabric, shaping the architecture and engineering practices that underpin enterprise‑wide data access, governance, and insight. Your team's work will directly enable Power BI reporting, advanced analytics, and emerging AI capabilities across the organization.

This is a high‑impact opportunity for a strong people leader who is also deeply technical in modern data platforms. Success requires proven management experience—coaching, mentoring, and growing engineers—combined with hands‑on technical leadership in data engineering, data modeling, and cloud‑based data services. You'll set the technical direction for McKinstry's data platform, drive governance and quality standards, and collaborate across analytics, application, and business teams.

This role is based in Seattle, WA and operates on a hybrid work schedule.

• In collaboration with Business Technology leadership, lead the design, development, and operation of McKinstry's Azure Data Lakehouse, including data ingestion, transformation, storage, and serving layers.
• Provide insights to guide the platform migration from Azure Synapse Analytics to Microsoft Fabric, ensuring continuity, performance, and scalability throughout the transition.
• Oversee the development of robust ETL/ELT pipelines using Azure Data Factory, Synapse Pipelines, and Fabric Dataflows to move and transform data across the enterprise.
• Ensure data models follow established patterns such as medallion architecture (bronze/silver/gold) and dimensional modeling (star schema) to support analytics and reporting.
• Partner with Business Tech Analytics management and Power BI developers and analysts to ensure the data platform delivers reliable, performant semantic models and datasets for enterprise reporting.
• Ensure solutions are designed with scalability, performance, security, and reliability in mind—particularly where data enables advanced analytics and AI initiatives.
• Participate in Agile ceremonies, backlog grooming, sprint planning, and cross‑functional coordination activities.

• Act as a data platform subject matter expert within McKinstry's technology organization, providing technical guidance on Azure data services and architecture.
• Define and guide data engineering standards, patterns, and best practices across the Lakehouse platform, including data pipeline design, data quality frameworks, and testing strategies.
• Lead the adoption of Microsoft Purview for data governance, cataloging, lineage tracking, and compliance across the enterprise data estate.
• Consult with Business Technology as they drive Master Data Management (MDM) strategy and implementation, ensuring consistent, trusted data entities across systems and platforms.
• Evaluate and guide the use of Azure data services including Azure Data Lake Storage, Azure Data Factory, Azure Synapse Analytics, Microsoft Fabric, and Azure Databricks where appropriate.
• Partner closely with leadership to inform decisions related to people, tools,…