Note: The job is a remote job and is open to candidates in USA. Metis Strategy is a management and strategy consulting firm focused on the intersection of business and technology. The Engineering Manager leads engineering teams delivering client data and AI systems, manages the full technical delivery lifecycle, develops engineering talent, and helps build the firm’s engineering practice through reusable frameworks, tooling, and technical leadership.
Responsibilities
- 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
- 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
- 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
- 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
- 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
- Build and enforce engineering practices — code quality, testing, CI/CD, observability, and documentation — so what we deliver stays maintainable long after the engagement ends
- 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
Skills
- 7–10+ years of relevant software or data engineering experience, including at least 2–3 years directly managing engineers; consulting, professional services, or other client-facing technical delivery experience is a plus
- 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
- Located within the Pacific time zone. Day-to-day, we work fully remotely, but some clients require travel to their offices. We host quarterly in-person offsites to maintain our collaborative culture
- 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
- Eligibility to work in the U.S. without sponsorship is preferred
Benefits
- This position is also eligible for bonus compensation.
- Comprehensive medical insurance, dental insurance, and vision insurance
- Parental leave, and family benefits
- Retirement contributions
- Stipend for continued learning
- Paid time off
- Day-to-day, we work fully remotely
Company Overview
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