Engineering Manager
for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). We should not repeat job title as heading. We need to include sections only when posting has real specific content. We have content: About Metis Strategy (company overview) could be "About the role"? Actually "About Metis Strategy" is about company, not role. Could be "About the role" maybe not present. We have "About This Position" which describes role. So we can use About the role
with that content. Then responsibilities: many bullet points. Then qualifications: Basic Qualifications and What We Look for (maybe combine into Requirements/Qualifications). Then Benefits: list of benefits. Then Pay: we have pay range. Then Schedule: not specified (location remote, travel). There's mention of location: Pacific time zone, fully remote, some travel, quarterly offsites. That's schedule? Not exactly schedule but could be considered Schedule. However instruction: never create Pay/Schedule/Benefits sections unless posting actually states them. Pay is stated. Benefits are stated. Schedule? Not explicitly schedule like hours, but we have location and remote work; maybe we can treat as Schedule? Might be considered "Location" not schedule. Safer to omit Schedule section if not explicit. We'll include Pay and Benefits.
We must not include boilerplate like EEO, etc. Not present.
We need to preserve all substantive info. Use - for lists. Use
for prose.
We must not repeat job title as heading. No Job ID.
We need to output only HTML fragment.
Let's craft:
About the role
As a senior member of our team, the Engineering Manager leads the engineers who build and deliver our clients' data and AI systems. You're a hands-on technical leader, a people manager, and a key contributor to how Metis builds its engineering practice. 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.
Then maybe a subsection "In Addition To Core Engineering Leadership..." but could be part of responsibilities. Could incorporate into responsibilities list.
Better to split responsibilities into bullet list.
Let's extract responsibilities bullet points:
- Lead one or more engineering teams through client engagements, turning data and AI strategy into working systems: data platforms, pipelines, AI/ML applications, and supporting infrastructure.
- Manage the full delivery lifecycle: technical design and architecture decisions through implementation, code review, testing, and production hand-off, keeping delivery on scope, quality, and schedule.
- Hire, coach, and grow engineers; set technical standards; build feedback loops, career paths, and mentorship.
- Partner directly with client engineering and technology leaders as a peer; translate ambiguous business requirements into clear technical plans; communicate tradeoffs, risks, and progress to technical and executive stakeholders.
- Make and stand behind architecture and technology choices for data platforms and AI/ML systems (cloud-native data lakes/warehouses, pipeline orchestration, MLOps tooling), balancing technical correctness with client operability post-engagement.
- Build and enforce engineering practices: code quality, testing, CI/CD, observability, documentation.
- Contribute to Metis's own engineering capability: reusable accelerators, internal tooling, technical playbooks, frameworks for repeatable high-quality delivery.
- In addition: help grow technical footprint: bring entrepreneurial thinking to scaling engineering practice; develop reusable architecture patterns, playbooks, internal tooling; publish technical write-ups/case studies; take ownership of internal engineering tools/platforms.
We can combine into a .
Then Requirements/Qualifications: Basic Qualifications and What We Look for.
Basic Qualifications:
- Experience: 7–10+ years 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.
- Education: Bachelor's or Master's degree in Computer Science, Engineering, or related technical field, or equivalent practical experience.
- People Management: proven track record of hiring, developing, managing engineering teams through complex, fast-paced projects with tight timelines and multiple stakeholders.
- Location: Located within Pacific time zone; day-to-day fully remote, some client travel required; quarterly in-person offsites.
What We Look for (maybe Skills/Attributes):
- Leadership: balance of technical depth, confidence, humility, empathy; ability to coach engineers and earn trust with senior client stakeholders.
- Relevant Experience: concrete examples of having built and shipped production data or AI systems, not just designed them, and leading engineers who built them.
- Problem Solving: pragmatic, hands-on approach to unblocking teams and making architecture tradeoffs under real constraints of time, budget, existing systems.
- Communication Skills: ability to translate technical detail for executives and translate business goals into clear technical plan for engineers.
- Client Interactions: comfortable being technical voice before client engineering leaders and C-level stakeholders.
- Relationship Orientation: willingness to build trust with client teams whose systems you're inheriting or handing back.
- Motivation: entrepreneurial, outcome-oriented, comfortable with ambiguity of consulting engagements.
- Curiosity: genuine interest in modern data and AI engineering practices and how they apply across industries.
- Technology: 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, security and compliance considerations for enterprise data and AI systems; fluency in at least one modern data or programming stack (e.g., Python, Spark, SQL, or major cloud data platform); experience with CI/CD, infrastructure as code, modern engineering practices.
- Work Status: eligibility to work in the U.S. without sponsorship preferred.
We can combine into Requirements section.
Benefits:
- comprehensive medical insurance, dental insurance, and vision insurance;
- parental leave, and family benefits;
- retirement contributions;
- stipend for continued learning;
- paid time off, and more.
Pay:
- The Pay Range For This Role Is 140,000 - 220,000 USD per year (West Coast Office)
We should not include "commensurate with experience" etc. The posting says "Compensation and benefits are competitive and will be commensurate with each candidate’s qualifications and relevant work experience." That's filler; we should not include that as it's vague. We already have explicit pay range, so we can include that.
We must not create Schedule section.
Now produce HTML.
We need to wrap sections in headings and content.
Let's structure:
About the role
...
Responsibilities
- ...
...
Requirements
- ...
...
Benefits
- ...
...
Pay
The Pay Range For This Role Is 140,000 - 220,000 USD per year (West Coast Office)
Make sure no extra text.
Let's craft concise but preserve all info.
We need to ensure we don't lose any substantive info from About Metis Strategy paragraph at top. That is company overview; maybe we should include it under About the role? The instruction: About the role heading for role description. The company overview could be considered part of About the role? Might be better to include a short paragraph about company under About the role before responsibilities. However we already have About the role description; we could prepend company overview as part of that paragraph. But the instruction says "About the role" heading for role description; we can include company overview as part of that paragraph. It's substantive.
Let's include company overview in About the role paragraph.
Thus About the role paragraph: combine Metis Strategy overview and role description.
Let's write:
Metis Strategy, a management and strategy consulting firm focused on the intersection of business and technology, continues to grow and is looking for highly motivated individuals to join our San Francisco Bay Area team. We are trusted advisors to Fortune 500 and Global 1000 companies, helping them meet their most critical business objectives through tailored engagements and deep professional relationships. We work across industries on topics related to business & digital strategy and navigating organizational change. We are an entrepreneurial, fast-growing firm headquartered in the Washington, D.C. area with offices in the San Francisco Bay Area and Dallas, Texas. The firm’s presence from coast to coast allows us to serve clients throughout the United States and internationally. In our 20+ year history, we have established a track record of making lasting impacts on organizational performance and helping executives improve business outcomes. In a career with Metis Strategy you will experience high growth as you encounter new challenges with each client engagement; have a high degree of autonomy to craft the direction of your work; and become empowered to work directly with thought-leaders, C-level clients, and other decision-makers. As a senior member of our team, the Engineering Manager leads the engineers who build and deliver our clients' data and AI systems. You're a hands-on technical leader, a people manager, and a key contributor to how Metis builds its engineering practice. 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. In Addition To Core Engineering Leadership, Metis Strategy Offers Engineering Managers a Chance To Help Grow The Technical Footprint Of a Thriving Consulting Firm. For Example, You Might Firm
for prose. We must not repeat job title as heading. No Job ID. We need to output only HTML fragment. Let's craft:
About the role
As a senior member of our team, the Engineering Manager leads the engineers who build and deliver our clients' data and AI systems. You're a hands-on technical leader, a people manager, and a key contributor to how Metis builds its engineering practice. 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.
Then maybe a subsection "In Addition To Core Engineering Leadership..." but could be part of responsibilities. Could incorporate into responsibilities list. Better to split responsibilities into bullet list. Let's extract responsibilities bullet points: - Lead one or more engineering teams through client engagements, turning data and AI strategy into working systems: data platforms, pipelines, AI/ML applications, and supporting infrastructure. - Manage the full delivery lifecycle: technical design and architecture decisions through implementation, code review, testing, and production hand-off, keeping delivery on scope, quality, and schedule. - Hire, coach, and grow engineers; set technical standards; build feedback loops, career paths, and mentorship. - Partner directly with client engineering and technology leaders as a peer; translate ambiguous business requirements into clear technical plans; communicate tradeoffs, risks, and progress to technical and executive stakeholders. - Make and stand behind architecture and technology choices for data platforms and AI/ML systems (cloud-native data lakes/warehouses, pipeline orchestration, MLOps tooling), balancing technical correctness with client operability post-engagement. - Build and enforce engineering practices: code quality, testing, CI/CD, observability, documentation. - Contribute to Metis's own engineering capability: reusable accelerators, internal tooling, technical playbooks, frameworks for repeatable high-quality delivery. - In addition: help grow technical footprint: bring entrepreneurial thinking to scaling engineering practice; develop reusable architecture patterns, playbooks, internal tooling; publish technical write-ups/case studies; take ownership of internal engineering tools/platforms. We can combine into a- .
Then Requirements/Qualifications: Basic Qualifications and What We Look for.
Basic Qualifications:
- Experience: 7–10+ years 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.
- Education: Bachelor's or Master's degree in Computer Science, Engineering, or related technical field, or equivalent practical experience.
- People Management: proven track record of hiring, developing, managing engineering teams through complex, fast-paced projects with tight timelines and multiple stakeholders.
- Location: Located within Pacific time zone; day-to-day fully remote, some client travel required; quarterly in-person offsites.
What We Look for (maybe Skills/Attributes):
- Leadership: balance of technical depth, confidence, humility, empathy; ability to coach engineers and earn trust with senior client stakeholders.
- Relevant Experience: concrete examples of having built and shipped production data or AI systems, not just designed them, and leading engineers who built them.
- Problem Solving: pragmatic, hands-on approach to unblocking teams and making architecture tradeoffs under real constraints of time, budget, existing systems.
- Communication Skills: ability to translate technical detail for executives and translate business goals into clear technical plan for engineers.
- Client Interactions: comfortable being technical voice before client engineering leaders and C-level stakeholders.
- Relationship Orientation: willingness to build trust with client teams whose systems you're inheriting or handing back.
- Motivation: entrepreneurial, outcome-oriented, comfortable with ambiguity of consulting engagements.
- Curiosity: genuine interest in modern data and AI engineering practices and how they apply across industries.
- Technology: 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, security and compliance considerations for enterprise data and AI systems; fluency in at least one modern data or programming stack (e.g., Python, Spark, SQL, or major cloud data platform); experience with CI/CD, infrastructure as code, modern engineering practices.
- Work Status: eligibility to work in the U.S. without sponsorship preferred.
We can combine into Requirements section.
Benefits:
- comprehensive medical insurance, dental insurance, and vision insurance;
- parental leave, and family benefits;
- retirement contributions;
- stipend for continued learning;
- paid time off, and more.
Pay:
- The Pay Range For This Role Is 140,000 - 220,000 USD per year (West Coast Office)
We should not include "commensurate with experience" etc. The posting says "Compensation and benefits are competitive and will be commensurate with each candidate’s qualifications and relevant work experience." That's filler; we should not include that as it's vague. We already have explicit pay range, so we can include that.
We must not create Schedule section.
Now produce HTML.
We need to wrap sections in
- ... ...
- ... ...
- ... ...
headings and content.
Let's structure:
About the role
...
Responsibilities
Requirements
Benefits
Pay
The Pay Range For This Role Is 140,000 - 220,000 USD per year (West Coast Office)
Make sure no extra text. Let's craft concise but preserve all info. We need to ensure we don't lose any substantive info from About Metis Strategy paragraph at top. That is company overview; maybe we should include it under About the role? The instruction: About the role heading for role description. The company overview could be considered part of About the role? Might be better to include a short paragraph about company under About the role before responsibilities. However we already have About the role description; we could prepend company overview as part of that paragraph. But the instruction says "About the role" heading for role description; we can include company overview as part of that paragraph. It's substantive. Let's include company overview in About the role paragraph. Thus About the role paragraph: combine Metis Strategy overview and role description. Let's write:Metis Strategy, a management and strategy consulting firm focused on the intersection of business and technology, continues to grow and is looking for highly motivated individuals to join our San Francisco Bay Area team. We are trusted advisors to Fortune 500 and Global 1000 companies, helping them meet their most critical business objectives through tailored engagements and deep professional relationships. We work across industries on topics related to business & digital strategy and navigating organizational change. We are an entrepreneurial, fast-growing firm headquartered in the Washington, D.C. area with offices in the San Francisco Bay Area and Dallas, Texas. The firm’s presence from coast to coast allows us to serve clients throughout the United States and internationally. In our 20+ year history, we have established a track record of making lasting impacts on organizational performance and helping executives improve business outcomes. In a career with Metis Strategy you will experience high growth as you encounter new challenges with each client engagement; have a high degree of autonomy to craft the direction of your work; and become empowered to work directly with thought-leaders, C-level clients, and other decision-makers. As a senior member of our team, the Engineering Manager leads the engineers who build and deliver our clients' data and AI systems. You're a hands-on technical leader, a people manager, and a key contributor to how Metis builds its engineering practice. 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. In Addition To Core Engineering Leadership, Metis Strategy Offers Engineering Managers a Chance To Help Grow The Technical Footprint Of a Thriving Consulting Firm. For Example, You Might Firm