Analytics Engineer

We need to output HTML fragment with sections: headings

for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real specific content. We must not repeat job title. No metadata like Job ID. Remove boilerplate. We need to parse the posting: It includes About Greystar (company description) then Job Description Summary, then Job Description (detailed responsibilities), then What You'll Do (list of responsibilities), then What You'll Bring (requirements/qualifications), then How You Operate (maybe skills/attributes), then Domain Knowledge (preferred). Also maybe Benefits, Pay, Schedule not mentioned. So we should not create those sections. We need sections: About the role? Could be "About the role" summarizing the role description. Could use "About the role" heading with paragraph summarizing role. Then "Responsibilities" for duties (What You'll Do). Then "Qualifications" or "Requirements" for What You'll Bring. Then "Skills" maybe for How You Operate (self-directed etc). Could also have "Domain Knowledge" as a subsection under Qualifications? But we need to follow headings list: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We can choose which to include. We have content for About the role (summary). Responsibilities (What You'll Do). Requirements/Qualifications (What You'll Bring). Skills (How You Operate). Domain Knowledge maybe part of Qualifications. We must not create empty sections. So include About the role, Responsibilities, Requirements, Qualifications, Skills. Possibly combine Requirements and Qualifications? The instruction says use headings for those sections if content exists. We have both Requirements and Qualifications content; we could put Requirements as "Requirements" and Qualifications as "Qualifications". But they may overlap. Safer to have one section "Qualifications" that includes both required and preferred. But we have separate headings list; we can include both if we have distinct content. Let's see: What You'll Bring includes required qualifications (Analytics Engineering Excellence, AI Fluency, How You Operate). Domain Knowledge (preferred). So we could have "Requirements" for required, and "Qualifications" for preferred? Or "Skills" for How You Operate. Let's map: - About the role: summary paragraph from Job Description Summary and maybe first paragraph of Job Description. - Responsibilities: What You'll Do bullet list. - Requirements: maybe the bullet list under "What You'll Bring" that are required: Analytics Engineering Excellence, AI Fluency, plus maybe the bullet points under each. - Qualifications: maybe Domain Knowledge (preferred) plus maybe academic background etc? But academic background is under Requirements. Hmm. Better: Use "Requirements" for required qualifications (Analytics Engineering Excellence, AI Fluency, How You Operate). Use "Qualifications" for preferred (Domain Knowledge). Use "Skills" for technical skills list (SQL, Python, Databricks, etc). But the technical skills are also under Requirements. Could split. Let's extract: Required: - 3+ years in high-performing analytics, analytics engineering, or data team, owning work end to end. - Academic background in quantitative field or equivalent practical experience. - Advanced SQL and hands-on data modeling experience. - Strong hands-on Python skills. - Experience building/maintaining data models and pipelines on modern lakehouse/warehouse, ideally Databricks. - Exposure to ML techniques (classification, clustering, prediction, sentiment analysis, A/B testing). - Fluency with BI tool (Power BI, Tableau, Qlik). - Sound analytical judgment (experiment design, correlation vs causation). - AI Fluency: hands-on experience with AI coding tools (Claude Code, Cursor, Codex) in day-to-day workflow. - Understanding of LLMs and AI agents consuming data, making data product reliable. - Familiarity with LLM integration patterns (RAG, vector DB, MCP) a plus. - Awareness of AI governance considerations. - Depth behind speed: understand AI output, debug, defend, build yourself. - Expect to explain AI usage. How You Operate (soft skills): - Self-directed, resourceful, builder's bias to action, biased toward finishing, reliable on commitments, full-lifecycle owner, learn the business, clear communicator, scope-disciplined and collaborative. Domain Knowledge (preferred): - Experience in real estate, property management, financial services, asset management. - Familiarity with multi-source data environments heterogeneous formats varying quality. - Experience building data products that serve multiple (cut off). Thus we can have:

About the role

... summary ...

Responsibilities

• ... each bullet from What You'll Do ...

Requirements

• ... required qualifications bullets ...

Qualifications

• ... preferred domain knowledge ...

Skills

• ... technical skills list (SQL, Python, Databricks, ML, BI tools, AI coding tools) ...

But we might duplicate technical skills in Requirements and Skills. Better to put technical skills under Requirements, and Skills for soft skills? However the instruction list includes Skills as a possible heading. We can decide to put soft skills under Skills. Let's do: - Requirements: hard qualifications (experience, education, technical). - Skills: soft skills (self-directed, resourceful, etc). - Qualifications: preferred domain knowledge. Thus we need to extract. Let's craft About the role paragraph: combine from Job Description Summary and first paragraph of Job Description. Summary: "Greystar's D²AI organization (Data, Digital, and AI) is responsible for the platforms, processes, and practices that power analytics and AI across the company. This role sits on Decision Intelligence, the team within D²AI that turns that capability into better business decisions. The name is the mandate: we exist to make the company's decisions faster, sharper, and better informed, not simply to produce reports. We require AI fluency because this role sits at the intersection of data, technology, and business outcomes. That means understanding how AI systems are designed and operationalized, using AI-enabled tools in your day-to-day work, and partnering effectively with engineering, analytics, and business teams so the solutions we ship are reliable, responsible, and impactful. We hire for Greystar, not for a single team. You'll join a fast-paced group and work across many initiatives as we modernize and rethink how the company operates. That range is the benefit: broad exposure to the business, real variety in the problems you solve, and the chance to help shape a multi-billion dollar global operator rather than maintain one corner of it. The people who thrive here are versatile, self-directed, and able to pick up unfamiliar business context quickly. Decision Intelligence reports through Technology, but each pod's priorities and success are defined by the business it serves, not by the technology organization. Instead of a central intake queue, the team organizes into small, forward-deployed pods: analysts and engineers who sit alongside one another, each pod devoted to a critical area of the business such as Marketing, Property Operations, Resident, or FP&A. Pods become a standing part of that business rather than a rotating project resource. The best of what a pod builds graduates onto shared platforms, so a win in one area of the business becomes a capability for the whole company." Then Job Description paragraph: "Greystar is building the data foundation that will power the most AI-advanced operator in global multifamily real estate. As an Analytics Engineer on one of Decision Intelligence's forward-deployed pods, you'll turn that foundation into working data products that a business team uses to make decisions every day, whether those are traditional dashboards, predictive models, or lightweight web apps. Our team includes engineers, designers, and product leaders with experience from Google, Microsoft, Airbnb, Strava, and Amazon. You'll spend most of your time devoted to one critical area of the business rather than behind a ticket queue. You'll learn its data, workflows, and real pain points firsthand, then use SQL, Python, Databricks, and AI tooling to build the product that solves the actual problem. AI is core to how this team works, not an afterthought: you're expected to use it daily to build faster, and we'll actively support you in doing that well. In practice, that means pods regularly ship predictive models and working web applications, the kind of work that would have required a dedicated engineering team not long ago. Once you pick up an initiative, we expect you to own your piece from question through delivery, hand it off cleanly, and move to the next. Some initiatives will play to a deep specialty; others will ask you to learn a new part of the business fast. Comfort with that kind of movement is part of the job. As your understanding of the business deepens, you'll grow from answering its questions to bringing it recommendations of your own, and you'll be a confident, trusted voice in the room, from working sessions with on-site operators to briefings with senior leaders and executives." We can combine into About the role paragraph (maybe two paragraphs). Use

for prose. Now Responsibilities: list from What You'll Do: - Own Initiatives End to End: Take assigned initiatives from the original business question through a data product people actually use to decide, staying with the work through deployment and handoff. - Embed inside your assigned area of the business, learning its goals, data, and workflows well enough to spot the highest-value opportunities yourself. - As your understanding deepens, bring the business proactive recommendations, not just answers to the questions it already knows to ask. - Default to doing it right; when speed is genuinely required, ship a usable solution with a documented path back to the governed, certified standard. - Build Data Products That Drive Decisions: Ship a working data product quickly, ranging from a dashboard to a statistical model to a lightweight web app, then iterate live with the people who will use it. - Build and maintain the data models behind those products in Databricks and SQL, with real rigor around grain, keys, and referential integrity so results hold up under scrutiny. - Design sound experiments and measurement plans to establish baselines and prove out program impact, keeping correlation and causation distinct. - Present findings, trade-offs, and recommendations to stakeholders ranging from on-site operators to senior leaders and executives, tailoring the message to the audience. - Identify which products are worth graduating to shared platforms such as the Greystar Performance System (GPS), our platform for enterprise reporting, and Podium, our internally built platform for enabling and governing AI use. - Partner with platform teams to scale them enterprise-wide. - Contribute reusable patterns, tooling, and documentation that raise the speed and quality of every pod. We treat documentation as part of delivery, not an afterthought. - Work AI-First: Use AI tools and techniques, including LLMs, AI coding assistants, and automation, to build faster yourself and to design smarter, more efficient solutions for the business. - Build data models and products that AI tools can consume reliably, including work that surfaces through MCP (Model Context Protocol) integrations and other LLM-powered interfaces. - Evaluate and adopt AI-powered analytics tooling, from AI-assisted cataloging to intelligent data quality monitoring. - Collaborate with other engineers and analysts on AI integration patterns, prompt engineering, and modern development practices. - We are an AI-forward team and it's moving fast, so we test, iterate, share, and repeat. - Drive Data Quality and Trust: Treat data quality as a core part of the job, not someone else's problem. - Validate the data behind every product you ship, and build in testing, monitoring, and anomaly detection so problems surface before business users find them. - When data is wrong, incomplete, or untrustworthy, raise it clearly and navigate the organization to get it resolved, working across data engineering, source system owners, and business partners until the root cause is fixed. - Follow data governance practices including access controls, PII handling, and appropriate use of data in AI systems. - Document