Engineering Manager

Metis Strategy is hiring an Engineering Manager to lead engineers delivering clients’ data and AI systems, from technical design through production hand-off. The role also develops engineering talent and practices, advises client leaders, and builds reusable tools and capabilities for the firm’s engineering practice.

Responsibilities:

• As a senior member of our team, the Engineering Manager leads the engineers who build and deliver our clients' data and AI systems
• In this role, you will lead one or more engineering teams through client engagements, turning data and AI strategy into working systems: data platforms, pipelines, AI/ML applications, and the infrastructure that supports them
• You'll manage the full delivery lifecycle, from technical design and architecture decisions through implementation, code review, testing, and production hand-off, while keeping delivery on scope, on quality, and on schedule
• You will hire, coach, and grow engineers, setting technical standards and building the feedback loops, career paths, and mentorship that help a small but growing engineering bench develop
• You'll partner directly with client engineering and technology leaders as a peer, translating ambiguous business requirements into a clear technical plan, and communicating tradeoffs, risks, and progress to both technical and executive stakeholders
• You will make and stand behind architecture and technology choices for data platforms and AI/ML systems, including cloud-native data lakes and warehouses, pipeline orchestration, and MLOps tooling, balancing what's technically right against what a client's team can realistically operate after we leave
• You'll build and enforce engineering practices — code quality, testing, CI/CD, observability, and documentation — so what we deliver stays maintainable long after the engagement ends
• And you'll contribute to Metis's own engineering capability: reusable accelerators, internal tooling, technical playbooks, and the frameworks that let the firm deliver technology work repeatably and at higher quality
• Bring entrepreneurial thinking to how a growing technology consulting firm builds and scales its engineering practice
• Help develop reusable architecture patterns, playbooks, and internal engineering tooling
• Turn engagement-based technical work into publishable technical write-ups and case studies
• Take ownership of internal engineering tools and platform capabilities that other consultants and clients rely on

Requirements:

• 7–10+ years of relevant software or data engineering experience, including at least 2–3 years directly managing engineers
• Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience
• A proven track record of hiring, developing, and managing engineering teams through complex, fast-paced projects with tight timelines and multiple stakeholders
• A balance of technical depth, confidence, humility, and empathy, with the ability to coach engineers and earn trust with senior client stakeholders
• Concrete examples of having built and shipped production data or AI systems, not just designed them, and of leading the engineers who built them
• A pragmatic, hands-on approach to unblocking teams and making architecture tradeoffs under real constraints of time, budget, and existing systems
• Ability to translate technical detail for executives and translate business goals into a clear technical plan for engineers
• Comfortable being the technical voice in front of both client engineering leaders and C-level stakeholders
• Willingness to build trust with client teams whose systems you're inheriting or handing back
• Entrepreneurial, outcome-oriented, and comfortable with the ambiguity of consulting engagements
• Genuine interest in modern data and AI engineering practices and how they apply across industries
• Hands-on experience with modern data platforms and architectures (cloud-native data lakes, warehouses, streaming and batch pipelines)
• experience with MLOps or AI deployment patterns
• working knowledge of data governance, data quality, and the security and compliance considerations for enterprise data and AI systems
• fluency in at least one modern data or programming stack (for example Python, Spark, SQL, or a major cloud data platform)
• experience with CI/CD, infrastructure as code, and modern engineering practices
• consulting, professional services, or other client-facing technical delivery experience is a plus
• Eligibility to work in the U.S. without sponsorship is preferred