Director of Data & Analytics

We need to output HTML fragment with sections: About the role? Actually we need headings for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Use

for each section heading. Only include sections where posting has real specific content. We need to extract content. We have job description: Position: Director of Data & Analytics, location, status, reports to, direct report, salary range, dress code, About Us, The Opportunity, What you'll own, How you'll work, What You Bring To The Table (Required), Location & Hours, Compensation, Benefits, The Interview Process, How We Hire (EEO etc). We need to remove boilerplate: cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, application instructions. So we should omit Interview Process and How We Hire (EEO). Also remove dress code? Not a heading; maybe include as prose? Not required heading. We can include in About the role? Probably not needed. We'll keep substantive info: About Us (company description), The Opportunity (role overview), What you'll own (responsibilities), How you'll work (work style), What You Bring To The Table (requirements/qualifications/skills), Location & Hours (location, schedule), Compensation (salary, bonus), Benefits (list). Also maybe include direct report and reports to? Could be part of About the role? Might be included in a short paragraph. We must not repeat job title as heading. So no

Director of Data & Analytics. We need to use

• for any list of duties/requirements/benefits. Use

  for prose. We must not create sections with filler like "commensurate with experience". Ensure we have actual content. Let's draft sections: - About the role? Could be a brief summary: maybe combine About Us and The Opportunity? But we need headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. However we can choose only those with content. The instructions: "use

  for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule)." It suggests these are examples; we should use them when appropriate. But we also must "include a section ONLY when the posting has real, specific content for it". So we can decide which of those headings have content. We have content for: - About the role: could include About Us and The Opportunity (role overview). That's substantive. - Responsibilities: What you'll own (list of responsibilities). Good. - Requirements: What You Bring To The Table (required experience). Could be Requirements. - Qualifications: maybe same as Requirements? Might be redundant. Could combine? But we could have Qualifications for education? Not present. Could skip Qualifications if no specific content. - Skills: maybe list of technical skills (SQL, Python, dbt, etc.) from Requirements. Could be Skills. - Benefits: Benefits section. - Pay: Compensation section. - Schedule: Location & Hours includes typical hours, possible extended hours, travel expectation. That's schedule. Thus we can include sections: About the role, Responsibilities, Requirements, Skills, Benefits, Pay, Schedule. We need to avoid duplication. Ensure we preserve all substantive info. Let's extract content. About Us paragraph: "Fuego Leads is a technology-focused health insurance company. We generate consumer demand through our own websites and national advertising, operate our own agency and call center, and distribute leads and live call transfers to the partners who buy from us. We are also building a direct-to-consumer brand of our own. We build our own technology. Our engineering team has developed proprietary dialing, SMS, and call center systems, along with quoting tools and the agent-facing interfaces our agency runs on. Our philosophy is to provide a quality service to our clients, offering an easy and seamless experience from start to finish. Compliance and customer experience are treated as core to the business rather than as constraints on it. Founded in 2019, Fuego is headquartered in Fort Lauderdale, Florida, with shared office space in Chicago, Illinois and Columbus, Ohio. At Fuego Leads, we recognize that our most valuable asset is our people. We prioritize creating an inclusive and diverse workforce, where every individual feels valued, respected, and empowered." The Opportunity paragraph: "Fuego's data function is moving from business reporting to the operational layer that executive decisions, marketing programs, and our AI systems run on. This role owns that layer end to end: the architecture, the definitions, the models, and the roadmap — and builds it hands-on through the first year. Our stack today runs on SQL Server in production with BigQuery as the warehouse, and reporting across both an in-house application and Sigma. You'll set the target-state design and sequence the work to get there." We could combine these into About the role. Responsibilities: list from "What you'll own". Items: - Core metric definitions — lead, contact, conversion, placement, CAC, LTV — including the calculations, the governance, and a named owner for each. - A certified core layer in BigQuery covering lead lifecycle, call and contact activity, agent and team performance, and media and channel spend, with exec-level reporting running on it. - The target-state data architecture across source systems, warehouse, and transformation layer, plus the data engineering roadmap that delivers it. - Multi-touch attribution across acquisition channels, resolved through to policy outcome and accurate enough to reallocate media and partner spend on. - A unified customer database, with identity resolved across systems into one durable record — the basis for LTV, renewals, persistency, cross-sell, and lifecycle marketing. - Fuego's ML roadmap — scoring, prioritization, routing — along with the platform to develop, deploy, monitor, and retrain on. Model work sits with an external vendor and a senior data engineer today; you'll bring it in-house and own model quality. - The data layer our AI systems retrieve from: schemas, identity resolution, and real-time availability. Every call is captured as a transcript with LLM scorecard values on top, and those need to be modeled and joined to customer, lead, and agent records. - Lead-quality economics, measured from acquisition cost through downstream buyer outcome. - Technical evaluation of AI platforms and vendors for the business more broadly. We can put each as

  . How you'll work: could be part of Responsibilities or separate? Might be considered "How you'll work" but not a required heading. Could be included in Responsibilities as prose. But we have a section for Skills maybe. What You Bring To The Table (Required): list: - 8+ years in data and analytics, including ownership of the design of a production warehouse and transformation layer, not only work within someone else's. - Expert SQL and production Python — pipelines, model code, and automation that other people depend on. - A modern transformation framework such as dbt, and the engineering practice that comes with it: version control, testing, code review, and documented lineage. - Managed a cloud warehouse run in production. BigQuery is ours; Snowflake, Redshift, or Databricks translate. - Models you've deployed and kept running, including monitoring and retraining. - Multi-touch attribution you've built and put in front of a media budget, and customer identity resolution across systems that weren't designed to share a customer record. - Substantive use of AI tooling in your own delivery. Be ready to walk us through something specific you built with it and where you stopped trusting it. - Effective written communication where an exec can read your recommendation once and act on it. Also "You'll do well if…" bullet points: these are more cultural fit; could be included in Skills or Requirements? Might be considered "Skills". We'll include them as part of Skills or Requirements? Probably as part of Skills (soft skills). We'll treat them as part of Skills. Location & Hours: content: - This role can be based remotely within the United States, or hybrid from the Fort Lauderdale, Dallas, Chicago, or Columbus metro areas. - Typical hours are 9 AM to 5 PM in your local time zone, Monday through Friday. - Possible extended hours during the enrollment period. - Some travel to our Fort Lauderdale office should be expected for remote-based hires. Compensation: - Base salary: $185,000 to $210,000 annually, plus a 10–20% target bonus. - Total target cash compensation of $203,500 to $258,000. - Base reflects market benchmarks for the scope of the role. - A metro adjustment applies: Fort Lauderdale and Columbus support the lower end of the band, Dallas the middle, and Chicago the upper end. - A fully remote hire is placed against a single national band. Benefits: - Competitive compensation (maybe redundant) - 20 days PTO accrued per calendar year - Company-sponsored health, dental, and vision insurance after a 3-month probationary period (optional) - 401(k) + matching after a 3-month probationary period (optional) - Great career development opportunities - Weekly team lunches We should not repeat "Competitive compensation" as benefit if already covered in Pay; but it's in benefits list. Could keep but maybe it's redundant. We'll keep as part of Benefits. Now produce HTML. Structure:

  About the role

  ... combined About Us and The Opportunity ...

  Responsibilities

  • ... each responsibility ...

  Requirements

  • ... each required experience ...

  Skills

  • ... technical skills from requirements plus soft skills ...

  But we may double-list. Perhaps better: Requirements for experience, Skills for technical and soft skills. Let's split. Requirements list: the 8+ years, ownership of design, expert SQL/production Python, managed cloud warehouse, models deployed, multi-touch attribution built, identity resolution, substantive use of AI tooling, effective written communication. Skills list: modern transformation framework such as dbt (version control, testing, code review, documented lineage), expertise with BigQuery/Snowflake/Redshift/Databricks, AI/ML platform evaluation, etc. plus the "You'll do well if…" bullets. Let's extract those. From Requirements bullet list we have: - 8+ years in data and analytics, including ownership of the design of a production warehouse and transformation layer, not only work within someone else's. - Expert SQL and production Python — pipelines, model code, and automation that other people depend on. - A modern transformation framework such as dbt, and the engineering practice that comes with it: version control, testing, code review, and documented lineage. - Managed a cloud warehouse run in production. BigQuery is ours; Snowflake, Redshift, or Databricks translate. - Models you've deployed and kept running, including monitoring and retraining. - Multi-touch attribution you've built and put in front of a media budget, and customer identity resolution across systems that weren't designed to share a customer record. - Substantive use of AI tooling in your own delivery. Be ready to walk us through something specific you built with it and where you stopped trusting it. - Effective written communication where an exec can read your recommendation once and act on it. Now "You'll do well if…" bullets: - You've built new capability and cleaned up what came before it on the same timeline. - You've built a data layer that an AI or LLM application retrieves from, and you know how that differs from designing for a dashboard. - You'd rather prototype something in a week and learn from it than scope it for a quarter. - You're happy owning scope rather than headcount. - One person reports to you today, and the team grows with what you build. - You've held a metric definition against a team that preferred a different number, and kept the working relationship.