Engineering Manager, Billing & Monetization

We need to output HTML fragment with sections for headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real specific content. We must not repeat job title. Must not include Job ID etc. Must preserve all substantive info. Remove boilerplate. We need to parse the posting. Sections present: - About The Role (intro paragraph) - Why DataSnipper? (maybe part of About the role? Could be separate but not required heading; we can include under About the role as prose.) - What You Will Do (Responsibilities) - What You Bring (Requirements/Qualifications/Skills) - Preferred Qualifications (maybe under Qualifications or Skills) - What we offer (Benefits) - Compensation Range (Pay) - No explicit Schedule but there is "Hybrid mode of work (at least 3 days onsite in our New York City office)" which is schedule info. So we can include Schedule section. We need to decide mapping: Use headings exactly as given: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We need to allocate content. About the role: include the intro paragraph(s) about role, why DataSnipper, etc. Could combine "About The Role" and "Why DataSnipper?" as prose. Responsibilities: content under "What You Will Do". Use bullet list. Requirements: maybe "What You Bring" core requirements (at least 3 years leading, 5 years software engineer, seasoned user of AI dev tools, experience building billing/metering, empowerment style, product aptitude, strong understanding, experience operating systems, track record growing engineers, strategic thinker, entrepreneurial mindset). Could be Requirements. Qualifications: maybe "Preferred Qualifications". Could be Qualifications. Skills: maybe combine some skill-specific items like Python, Azure, observability, testing strategies, etc. Could be Skills. Benefits: "What we offer" list. Pay: Compensation Range. Schedule: Hybrid mode of work (at least 3 days onsite). We must not include filler like "commensurate with experience". Not present. We must ensure we don't create empty sections. Let's extract content. About the role paragraph: "We are looking for a skilled and passionate Engineering Manager to join our Billing & Monetization Team in the New York City office. Working in a bootstrapped and profitable scaleup, you'll have the unique opportunity to lead the team that powers how our agentic AI products are metered, priced, and billed, building the systems that turn AI usage into reliable, transparent revenue for a fast-growing category-defining company. Why DataSnipper? Audit and finance are still massively manual, and we're changing that. DataSnipper is a $1B, bootstrapped unicorn with 600,000+ users across 180+ countries, already embedded in the daily workflows of top audit and accounting firms. Now, we're bringing AI directly into where the work actually happens through our agentic products, and monetizing AI usage at scale is central to how we grow. As our agentic products expand, so does the complexity of measuring and monetizing them. Credit consumption, usage-based billing, and transparent pricing are core to the business, and the systems behind them need to be accurate, real-time, and trustworthy. If you want to own the infrastructure where AI usage meets revenue at scale, this is the place." That's the About the role. Responsibilities (What You Will Do) list items: - Lead an engineering team through outcomes, not tasks; set clear goals with the team and trust them to figure out how. - Make the team measurably better; raise the engineering bar, grow engineers into bigger problems, and address performance issues directly when they arise. - Drive a step change in what the team can do with AI; not just shipping faster, but tackling harder problems, raising quality, and expanding what the team can credibly own. - Lead the team responsible for building the credit, metering, and billing systems that power monetization across our agentic AI products, from usage tracking and credit consumption to invoicing and revenue reporting. - Own the technical direction and architecture of the billing and monetization platform, ensuring metering is accurate, billing is reliable, and the systems are observable, secure, and auditable. - Guide the team in designing and scaling systems that meter AI and agent usage in real time, enforce credit limits and entitlements, and integrate with pricing, payments, and financial systems. - Partner closely with Product, Finance, and other Engineering teams to shape pricing and packaging, and translate monetization strategy into robust technical implementation. - Make the architectural and technical calls that shape the team's systems, and stay close enough to the code to do so credibly. - Lead the adoption of AI-assisted engineering practices, including AI coding tools and agentic coding harnesses, and set the bar for how the team uses them to drive outcomes; contribute to how DataSnipper leverages AI across the broader engineering organization. - Help shape the team's scope as it evolves, including expanding ownership areas and standing up new teams when needed. - Foster a culture of experimentation, learning, documentation, and operational excellence. We'll put each as

. Requirements (What You Bring) core items: - At least 3 years of leading and managing engineering teams, driving performance, growth, and professional development through hands-on mentorship, feedback, and goal setting. - Minimum 5 years as a software engineer with a passion for building scalable, reliable, and high-quality software systems. - Seasoned user of AI development tools yourself, with hands-on fluency in AI-assisted coding and agentic coding harnesses, and a track record of leading engineering teams that build with these tools day to day and are measurably more effective as a result. - Experience building billing, metering, payments, or monetization systems, ideally involving usage-based or credit-based pricing at scale. - An empowerment style of management; you lead through outcomes and trust, not by assigning tasks. - Strong product aptitude; can challenge product framing, translate business and pricing needs into technical solutions, and make effective roadmap tradeoffs. - Strong understanding of software architecture, distributed systems, APIs, and cloud native development. - Experience operating systems in production with a focus on accuracy, reliability, observability, and maintainability, especially where financial correctness matters. - A track record of growing engineers and creating high-performing teams. - Strategic thinker; able to shape product direction, technical direction, and team scope, not just execute on what's handed down. - Entrepreneurial mindset with the ability to thrive in a fast-moving scale-up environment. We'll list as

. Qualifications (Preferred Qualifications) list: - Experience building usage-based, metered, or credit-based billing systems in production. - Experience integrating with payment providers, billing platforms (e.g. Stripe), or ERP and financial systems. - Familiarity with monetizing AI or agentic products, including token or usage metering and cost attribution. - Experience with Python and modern backend development practices. - Understanding of financial correctness concerns such as idempotency, reconciliation, auditability, and revenue recognition. - Familiarity with Azure and cloud infrastructure. - Experience with observability and monitoring tools. - Experience working in startups or scaleups. - Experience in fintech, audit technology, or document-intensive workflows. - Strong understanding of testing strategies and engineering quality practices. We'll list as

. Skills: maybe we could extract some skill-specific items not covered elsewhere: maybe "Strong product aptitude", "Strong understanding of software architecture", etc. But those are already in Requirements. Could create Skills section for technical skills: Python, Azure, observability tools, testing strategies, etc. However we already have those in Preferred Qualifications. To avoid duplication, we could keep Skills empty? But we must not create empty sections. Perhaps we can combine Skills with Qualifications? But the instruction says use headings only when posting has real specific content for it. We could decide not to include Skills section if content overlaps; but we need to decide. We have content for Skills: maybe "Strong product aptitude; can challenge product framing, translate business and pricing needs into technical solutions, and make effective roadmap tradeoffs." That's more of a skill. Also "Strong understanding of software architecture, distributed systems, APIs, and cloud native development." Also "Experience operating systems in production with a focus on accuracy, reliability, observability, and maintainability". Also "Experience with Python and modern backend development practices." Also "Familiarity with Azure and cloud infrastructure." Also "Experience with observability and monitoring tools." Also "Strong understanding of testing strategies and engineering quality practices." We could allocate Skills section to capture those technical skill items, while Requirements focuses on experience/leadership years, etc. Qualifications could be preferred qualifications. Let's split: Requirements (mandatory): years of experience, leadership, empowerment style, product aptitude? Actually product aptitude could be skill. But we can keep. Qualifications (preferred): the list under "Preferred Qualifications". Skills: maybe combine some skill-specific items from What You Bring and Preferred Qualifications that are skill-focused: "Strong product aptitude", "Strong understanding of software architecture, distributed systems, APIs, and cloud native development", "Experience operating systems in production with a focus on accuracy, reliability, observability, and maintainability", "Experience with Python and modern backend development practices", "Familiarity with Azure and cloud infrastructure", "Experience with observability and monitoring tools", "Strong understanding of testing strategies and engineering quality practices". However many of these appear also in Preferred Qualifications. To avoid duplication, we could decide not to have Skills section and instead put those under Qualifications. But we need to follow instruction: "Prefer a few substantive sections over many thin ones." So maybe we combine Qualifications and Skills into one section called Qualifications (or Skills). But we have to use the headings exactly as given; we cannot rename. We could decide to omit Skills section if we don't have distinct content not already covered. However we have some skill-specific content not in Preferred Qualifications: "Strong product aptitude" is not in Preferred Qualifications. Also "Strong understanding of software architecture, distributed systems, APIs, and cloud native development" is somewhat similar to "Familiarity with Azure and cloud infrastructure" but not identical. "Experience operating systems in production with a focus on accuracy, reliability, observability, and maintainability" is not in Preferred Qualifications. So we could create Skills section for those. Thus: - Requirements: mandatory experience/leadership. - Qualifications: preferred qualifications list. - Skills: skill-specific items. Let's extract Skills items from the text: From What You Bring: - Strong product aptitude; can challenge product framing, translate business and pricing needs into technical solutions, and make effective roadmap tradeoffs. - Strong understanding of software architecture, distributed systems, APIs, and cloud native development. - Experience operating systems in production with a focus on accuracy, reliability, observability, and maintainability, especially where financial correctness matters. From Preferred Qualifications: - Experience with Python and modern backend development practices. - Understanding of financial correctness concerns such as idempotency, reconciliation, auditability, and revenue recognition. (maybe skill) - Familiarity with Azure and cloud infrastructure. - Experience with observability and monitoring tools. - Experience working in startups or scaleups. (maybe experience) - Experience in fintech, audit technology, or document-intensive workflows. (experience) - Strong understanding of testing strategies and engineering quality practices. We could put skill items: Strong product aptitude, Strong understanding of software architecture..., Experience operating systems..., Experience with Python..., Familiarity with Azure..., Experience with observability..., Strong understanding of testing strategies..., Understanding of financial correctness concerns... But many of these overlap with Preferred Qualifications. To avoid duplication, maybe we just put all Preferred Qualifications under Qualifications, and Skills we could leave out or combine. However we need to avoid empty sections. Let's see if we can create a meaningful Skills section with items not already in Qualifications: Strong product aptitude, Strong understanding of software architecture..., Experience operating systems..., maybe also "Strong product aptitude" is a skill. We'll include those three. Thus Skills section will have three bullet points. Now