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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Analytics Engineer job in Chicago - **Company:** Greystar Real Estate Partners, LLC - **Location:** Chicago, IL, United States - **Experience:** Experienced - **Salary:** $92,000.0 - $130,000.0 - **Contract:** Permanent contract - **Skills:** Unity 3d, A/B Testing, Agile Methodology, Artificial Intelligence, Data Analysis, Application Frameworks, Microsoft Azure, Cluster Analysis, Continuous Integration, Information Engineering, Data Governance, Data Integrity, Cursor (Graphical User Interface Elements), Software Debugging, Global Positioning Systems (GPS), Python (Programming Language), PostgreSQL, Machine Learning, Power BI, Azure Machine Learning, Sentiment Analysis, SQL Databases, Tableau (Software), Web Applications, Datadog, Sql Optimization, Large Language Models, Prompt Engineering, Apache Spark, Git, Data Lakes, Information Technology, Data Analytics, Qlikview, Streamlit Framework, Databricks - **Published:** August 14, 2026 - **Apply:** https://jobs.diversity.com/career/2453285/analytics-engineer-illinois-il-chicago ## About the Role &bull 3+ years in a high-performing analytics, analytics engineering, or data team, with a track record of owning work end to end rather than executing assigned tasks. &bull Academic background in a quantitative field (Analytics, Computer Science, Applied Mathematics, Economics, Statistics) or equivalent practical experience. &bull Advanced SQL and hands-on data modeling experience, with a firm grasp of grain, keys, referential integrity, and what it takes to trust a data asset. &bull Strong, hands-on Python skills for data analysis, automation, and building tools, not just one-off scripting. &bull Experience building or maintaining data models and pipelines on a modern lakehouse or warehouse platform, ideally Databricks. &bull Exposure to machine learning techniques such as classification, clustering, prediction, sentiment analysis, and A/B testing. &bull Fluency with a business intelligence tool such as Power BI, Tableau, or Qlik. &bull Sound analytical judgment, including experiment design and a clear understanding of correlation versus causation. AI Fluency &bull Hands-on experience with AI coding tools such as Claude Code, Cursor, or Codex in your day-to-day workflow. &bull Understanding of how LLMs and AI agents consume data, and what it takes to make a data product reliable when an AI tool is the consumer. &bull Familiarity with LLM integration patterns is a plus, including RAG architectures, vector databases, and MCP or other tool-use frameworks. &bull Awareness of AI governance considerations: data provenance, appropriate scoping, and responsible AI data practices. &bull Depth behind the speed. You should understand what the AI produces well enough to read it, debug it, defend the approach, and build it yourself if you had to, even if that would take you considerably longer. Shipping work you cannot explain is not the bar., &bull Experience in real estate, property management, financial services, or asset management is a strong plus. &bull Familiarity with multi-source data environments where data arrives in heterogeneous formats with varying quality. &bull Experience building data products that serve multiple business units with different access and governance requirements. &bull Experience with Agile product development, design thinking, or prior work embedded with a business or client team. Tools & Technologies This stack is broader than a traditional analytics role would require, and that is deliberate. AI coding assistants have collapsed the ramp-up time on unfamiliar tools, and this team uses them daily to work well past where an analytics background alone would land. We do not expect depth in everything listed below. We do expect you to learn quickly and to understand what you ship well enough to stand behind it. &bull AI coding assistants (Claude Code, Cursor, Codex). This is the layer that makes the rest of this list reachable, and we treat it as core tooling rather than a nice-to-have. &bull SQL, Python, dbt or similar transformation frameworks. &bull Databricks, with exposure to Spark, Delta Lake, and Unity Catalog. &bull Power BI (primary), with Tableau or Qlik experience transferable. &bull Lightweight application frameworks such as Streamlit, Power Platform, or similar. &bull Exposure to Azure cloud services (ADLS, Azure ML, Synapse) or equivalent relational back ends such as Postgres. &bull Git, CI/CD, and collaborative development practices. &bull Data quality and observability tooling such as Great Expectations or Monte Carlo. &bull MCP, RAG frameworks, and LLM-powered analytics a plus. &bull Greystar platforms: GPS for enterprise reporting and Podium for AI enablement and governance. ## Description 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. What You'll Do Own Initiatives End to End &bull 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. &bull Embed inside your assigned area of the business, learning its goals, data, and workflows well enough to spot the highest-value opportunities yourself. &bull As your understanding deepens, bring the business proactive recommendations, not just answers to the questions it already knows to ask. &bull 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 &bull 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. &bull 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. &bull Design sound experiments and measurement plans to establish baselines and prove out program impact, keeping correlation and causation distinct. &bull Present findings, trade-offs, and recommendations to stakeholders ranging from on-site operators to senior leaders and executives, tailoring the message to the audience. &bull 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. &bull 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 &bull 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. &bull 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. &bull Evaluate and adopt AI-powered analytics tooling, from AI-assisted cataloging to intelligent data quality monitoring. &bull 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 &bull Treat data quality as a core part of the job, not someone else's problem. Our solutions are only as good as the data underneath them. &bull Validate the data behind every product you ship, and build in testing, monitoring, and anomaly detection so problems surface before business users find them. &bull 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. &bull Follow data governance practices including access controls, PII handling, and appropriate use of data in AI systems. &bull Document known limitations and caveats alongside every product, so the people using it understand what the data can and cannot tell them., Many factors go into determining employee pay within the posted range including business requirements, prior experience, current skills and geographical location. * Corporate Positions: In addition to the base salary, this role may be eligible to participate in a quarterly or annual bonus program based on individual and company performance. * Onsite Property Positions: In addition to the base salary, this role may be eligible to participate in weekly, monthly, and/or quarterly bonus programs. ## Related Videos - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Debugging in the Dark](https://www.wearedevelopers.com/videos/1658-debugging-in-the-dark) - [Shipping Faster with Less: Render on Cloud Hosting, AI Workloads, and the Future of DevOps](https://www.wearedevelopers.com/videos/1894-shipping-faster-with-less-render-on-cloud-hosting-ai-workloads-and-the-future-of-devops) - [Git for Code Reviews](https://www.wearedevelopers.com/videos/429-git-for-code-reviews) - [Navigating the AI Revolution in Software Development](https://www.wearedevelopers.com/videos/1266-navigating-the-ai-revolution-in-software-development) - [Software Engineering Social Connection: Yubo’s lean approach to scaling an 80M-user infrastructure](https://www.wearedevelopers.com/videos/1583-software-engineering-social-connection-yubo-s-lean-approach-to-scaling-an-80m-user-infrastructure) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [The State of WebDev AI 2025 Results: What Can We Learn?](https://www.wearedevelopers.com/magazine/581-the-state-of-webdev-ai-2025-results-what-can-we-learn) - [13 AI Tools You Have to Try](https://www.wearedevelopers.com/magazine/219-13-ai-tools-you-have-to-try)