Software Engineering Manager

Freddie Mac•McLean, VA

About The Position

Freddie Mac is seeking an experienced and motivated Software Engineering Manager to drive the strategic execution and delivery of the Multifamily Data Analytics & AI platform. This role ensures alignment with business objectives, seamless integration, compliance with regulations, and operational excellence. Freddie Mac's Multifamily division advances affordable rental housing by purchasing apartment loans from a national network of lenders and transforming those loans into securities that attract global investors. The Multifamily Technology Team plays a critical role by enabling Multifamily business teams to make faster, more consistent, and data-driven decisions through trusted analytics, insights, and AI-enabled capabilities. They also modernize data platforms to improve accessibility, operational efficiency, scalability, and confidence in data, while delivering secure, resilient, auditable, and well-architected platform capabilities.

Requirements

8-10 years of related software engineering, data engineering, or technology delivery experience, including at least 2+ years of people management and leadership experience developing high-performing engineering teams is required.
Bachelor’s degree in computer science, information systems, or a related technical discipline, or an equivalent combination of education, training, and work experience; advanced degree preferred.
Demonstrated experience leading multiple agile squads and delivering complex technology initiatives across data pipelines, reporting, data products, platform modernization, and AI-enabled solutions.
Required hands-on or technical leadership experience with modern cloud and data platform technologies, including AWS, Snowflake, Python, PySpark, and SQL; experience with Snowflake Cortex or similar AI-enabled data capabilities is preferred.
Strong background in data engineering, data platforms, data pipelines, data governance, data quality, and delivery of scalable data products for business and end-user needs.
Experience establishing and applying software engineering standards, including scalable design, code quality, automated testing, peer reviews, deployment discipline, compliance standards, auditability, and production readiness.
Proven ability to provide technical direction, evaluate solution options, guide architecture and implementation decisions, and create proofs of concept or demos to validate new ideas and emerging technologies.
Experience managing engineering production support responsibilities, including incident triage, root-cause analysis, resiliency improvements, observability, and continuous improvement of platform reliability.
Strong leadership skills, including hiring, coaching, mentoring, delegation, performance management, talent development, and succession planning.
Ability to manage dependencies with vendors, Product Owners, architecture teams, business stakeholders, and internal engineering teams to deliver software solutions effectively.

Nice To Haves

Advanced degree preferred.
Experience with Snowflake Cortex or similar AI-enabled data capabilities is preferred.

Responsibilities

Lead multiple agile squads responsible for delivering modern data platform capabilities, including data pipelines, reporting, data products, platform modernization, and AI-enabled solutions.
Provide technical direction across squads by guiding solution design, reviewing implementation approaches, and helping teams make scalable, resilient, and maintainable engineering decisions.
Partner closely with Product Owners, architecture, business stakeholders, and cross-functional technology teams to align priorities, manage dependencies, and deliver business outcomes.
Drive the design and implementation of high-performing data pipelines and data products using modern technologies such as AWS, Snowflake, Python, PySpark, SQL, and Snowflake Cortex.
Create proofs of concept, demos, and prototypes to evaluate emerging technologies, validate new ideas, and accelerate adoption of innovative data and AI capabilities.
Establish and reinforce software engineering standards, including scalable design, code quality, automated testing, peer reviews, deployment discipline, and operational readiness.
Ensure solutions are designed and delivered with appropriate compliance, auditability, data governance, data quality, security, resiliency, and production support considerations.
Oversee engineering production support by strengthening incident triage, root-cause analysis, observability, recovery practices, and continuous improvement of platform reliability.
Manage, mentor, and develop engineering talent through coaching, performance management, hiring, delegation, and succession planning.
Foster a culture of ownership, collaboration, continuous improvement, and accountability while identifying opportunities to improve delivery efficiency and engineering outcomes.