Engineering Manager

About the role

DAT DAT Freight & Analytics is an award-winning employer of choice and a next-generation SaaS technology company. Founded in 1978, DAT operates the largest freight marketplace in North America, processing 250 million+ load posts annually and maintaining one of the largest repositories of freight market transaction data in the world. The Engineering Manager for the Carrier Services Board (CSB) team leads a critical initiative to migrate CSB’s legacy codebase to a modern, cloud-native architecture on Kubernetes, ensuring continued value for customers without disruption.

Responsibilities

• Lead a team of 5–8 software engineers across different levels, fostering a culture of ownership, craftsmanship, and continuous improvement.
• Mentor, grow, and recruit exceptional engineers; build a diverse, high-performing team.
• Create individualized growth plans that align personal career goals with team and business needs.
• Resolve team conflicts and ambiguity proactively; protect the team’s focus and psychological safety.
• Operate within DAT’s Product Operating Model—partnering with Product Managers on discovery, prioritization, and roadmap planning to ensure engineering is a proactive voice in product direction.
• Own sprint planning, backlog refinement, and delivery forecasting; hold the team accountable to commitments while managing scope responsibly.
• Translate high-level business goals into well-scoped engineering milestones, removing blockers and managing dependencies across teams.
• Drive a culture of high-quality engineering practices: code review standards, test coverage, observability, and on-call readiness.
• Report on team health, delivery velocity, and technical health metrics to Engineering leadership.
• Lead the multi-phase migration of CSB’s legacy codebase to a modern, cloud-native architecture deployed on Kubernetes, with a disciplined approach to risk management and zero customer-impacting regressions.
• Collaborate with senior engineers and architects to define target-state architecture, sequencing migration work alongside new feature delivery.
• Champion containerization, infrastructure-as-code, and GitOps practices across the team.
• Drive adoption of modern observability and deployment tooling (CI/CD, Helm, Argo, or equivalent).
• Evaluate technical trade-offs with an eye toward long-term maintainability, developer experience, and operational cost.
• Partner with peer engineering managers, platform teams, and data engineering to ensure CSB systems integrate reliably with downstream consumers and upstream data sources.
• Represent the CSB team in engineering leadership forums; contribute to org-wide engineering standards, hiring committees, and technical guilds.
• Communicate technical status, risks, and decisions clearly to non-technical stakeholders including Product, Data, and Business leadership.

Qualifications

• 5+ years of software engineering experience, with at least 2 years in an engineering management or technical lead role managing direct reports.
• Strong working knowledge of the Product Operating Model or equivalent modern product development frameworks (continuous discovery, dual-track agile, outcome-based roadmaps).
• Practical experience leading a migration from a legacy codebase or monolith to a modern, service-oriented or microservices architecture.
• Fluency with CI/CD pipelines, DevOps practices, and the tooling ecosystem (Docker, Helm, Argo CD, Terraform, or equivalents).
• Solid understanding of cloud platforms (AWS) and distributed system design patterns.
• Excellent written and verbal communication skills; ability to operate at both strategic and tactical levels.
• Track record of recruiting and retaining strong engineers; skilled at providing direct, growth-oriented feedback.

Skills and Experience

• Experience in data-intensive systems—stream processing, high-throughput ingestion pipelines, or real-time analytics platforms.
• Familiarity with event-driven architecture, Kafka, or similar messaging infrastructure.
• Experience working with Snowflake, dbt, or modern data stack tooling.
• Exposure to ML/AI-adjacent engineering (model serving, feature pipelines, or experimentation infrastructure).
• Active participation in engineering communities—open-source contributions, writing, conference talks, or internal thought leadership.