Engineering Manager

Metis Strategy•Walnut Creek, CA

32d•$140,000 - $220,000•Remote

About The Position

The Engineering Manager leads engineers building and delivering clients’ data and AI systems. This role combines hands-on technical leadership, people management, and contributions to Metis Strategy’s engineering practice. The manager leads one or more teams through client engagements, translating data and AI strategy into data platforms, pipelines, AI/ML applications, and supporting infrastructure. The role oversees the delivery lifecycle, from technical design and architecture decisions through implementation, code review, testing, and production hand-off, ensuring work stays on scope, quality, and schedule. The Engineering Manager hires, coaches, and develops engineers; sets technical standards; and builds feedback loops, career paths, and mentorship. They partner with client engineering and technology leaders, translate ambiguous business requirements into technical plans, and communicate tradeoffs, risks, and progress to technical and executive stakeholders. The manager makes architecture and technology choices for data platforms and AI/ML systems, including cloud-native data lakes and warehouses, pipeline orchestration, and MLOps tooling. They establish engineering practices for code quality, testing, CI/CD, observability, and documentation, with an emphasis on maintainability after engagements end. They also contribute reusable accelerators, internal tooling, technical playbooks, frameworks, publishing, and internal platform capabilities that help Metis deliver technology work repeatedly and at higher quality. The position is fully remote day to day and requires residence within the Pacific time zone. Some client engagements require travel to client offices, and quarterly in-person offsites support the collaborative culture. The role is eligible for bonus compensation, and compensation is commensurate with qualifications and relevant experience.

Requirements

7–10+ years of relevant software or data engineering experience.
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.
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.
Concrete experience building and shipping production data or AI systems and leading the engineers who built them.
Pragmatic, hands-on problem-solving skills, including unblocking teams and making architecture tradeoffs under constraints involving time, budget, and existing systems.
Ability to communicate technical details to executives and translate business goals into clear technical plans for engineers.
Comfort serving as the technical voice with client engineering leaders and C-level stakeholders.
Ability and willingness to build trust with client teams whose systems are being inherited or handed back.
Entrepreneurial, outcome-oriented approach and comfort with the ambiguity of consulting engagements.
Hands-on experience with modern data platforms and architectures, including cloud-native data lakes, warehouses, and streaming and batch pipelines.
Experience with MLOps or AI deployment patterns.
Working knowledge of data governance, data quality, and security and compliance considerations for enterprise data and AI systems.
Fluency in at least one modern data or programming stack, such as Python, Spark, SQL, or a major cloud data platform.
Experience with CI/CD, infrastructure as code, and modern engineering practices.

Nice To Haves

Consulting, professional services, or other client-facing technical delivery experience is a plus.
Eligibility to work in the U.S. without sponsorship is preferred.

Responsibilities

Lead one or more engineering teams through client engagements delivering data and AI systems.
Manage the full delivery lifecycle, including technical design, architecture decisions, implementation, code review, testing, and production hand-off.
Keep delivery on scope, on quality, and on schedule.
Hire, coach, and grow engineers; set technical standards and develop feedback loops, career paths, and mentorship.
Partner with client engineering and technology leaders to translate ambiguous business requirements into clear technical plans.
Communicate technical tradeoffs, risks, and progress to technical and executive stakeholders.
Make and stand behind architecture and technology choices for data platforms and AI/ML systems.
Build and enforce practices for code quality, testing, CI/CD, observability, and documentation.
Contribute reusable accelerators, internal tooling, technical playbooks, and frameworks to strengthen Metis’s engineering capability.
Help develop reusable architecture patterns and publish technical write-ups and case studies based on engagement work.
Take ownership of internal engineering tools and platform capabilities used by consultants and clients.