Analytics Engineer

ABOUT GREYSTAR

Greystar is a leading fully integrated global real estate platform offering expertise in property management investment management development and construction services in institutional-quality rental housing. Headquartered in Charleston South Carolina Greystar manages and operates over $350 billion of real estate in more than 260 markets globally with offices throughout North America Europe South America and the Asia-Pacific region. Greystar is the largest operator of apartments in the United States managing over one million units/beds globally. Across its platforms Greystar has nearly $79 billion of assets under management including over $34 billion of development assets and over $36.5 billion of regulatory assets under management. Greystar was founded by Bob Faith in 1993 to become a provider of world-class service in the rental residential real estate business. To learn more visit .

JOB DESCRIPTION SUMMARY

Greystars 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 companys 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. Youll 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 pods 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.

JOB 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 Intelligences forward-deployed pods youll 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.

Youll spend most of your time devoted to one critical area of the business rather than behind a ticket queue. Youll 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: youre expected to use it daily to build faster and well actively support you in doing that 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 youll grow from answering its questions to bringing it recommendations of your own and youll be a confident trusted voice in the room from working sessions with on-site operators to briefings with senior leaders and executives.

What Youll 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 its 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 elses problem. Our solutions are only as good as the data underneath them.

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 known limitations and caveats alongside every product so the people using it understand what the data can and cannot tell them.

What Youll Bring

Analytics Engineering Excellence

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.

Academic background in a quantitative field (Analytics Computer Science Applied Mathematics Economics Statistics) or equivalent practical experience.

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.

Strong hands-on Python skills for data analysis automation and building tools not just one-off scripting.

Experience building or maintaining data models and pipelines on a modern lakehouse or warehouse platform ideally Databricks.

Exposure to machine learning techniques such as classification clustering prediction sentiment analysis and A/B testing.

Fluency with a business intelligence tool such as Power BI Tableau or Qlik.

Sound analytical judgment including experiment design and a clear understanding of correlation versus causation.

AI Fluency

Hands-on experience with AI coding tools such as Claude Code Cursor or Codex in your day-to-day workflow.

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.

Familiarity with LLM integration patterns is a plus including RAG architectures vector databases and MCP or other tool-use frameworks.

Awareness of AI governance considerations: data provenance appropriate scoping and responsible AI data practices.

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.

Expect us to ask in specifics how you use AI in your day-to-day workflow. It is a real part of how we evaluate candidates.

How You Operate

Self-directed: you take ambiguous requirements and drive them forward and you stay productive when priorities shift.

Resourceful: you figure it out. When you hit an unfamiliar source system or a dataset nobody seems to own you take the ambiguity head-on and draw on every resource available to you to work through it.

Builders bias to action: a functional first version in front of real users beats a perfect spec. You start building early and improve in the open rather than waiting for requirements to be fully settled.

Biased toward finishing without cutting corners: you would rather close something out and put it in front of people than carry three things at ninety percent. At the same time it runs is not the bar; reliability and quality are.

Reliable on your commitments: youre ambitious about what you take on and honest about what you can promise. When something is at risk you raise it early rather than letting a date slip quietly.

Full-lifecycle owner: you care how the work lands not just whether it shipped. You see a product through to the point where people trust it enough to make real decisions on and youll follow a problem past where the assignment technically ends.

You learn the business: you actively pick up the domain including real estate property management investment and financial data so your models reflect how the business actually works rather than just the shape of the source tables.

Clear communicator: you can explain analytical decisions and trade-offs to product managers operators analysts and executives tailoring the message to the audience.

Scope-disciplined and collaborative: you solve the problem in front of you without overengineering and you operate as one team across engineering product analytics and the business.

DOMAIN KNOWLEDGE (PREFERRED)

Experience in real estate property management financial services or asset management is a strong plus.

Familiarity with multi-source data environments where data arrives in heterogeneous formats with varying quality.

Experience building data products that serve multiple business units with different access and governance requirements.

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.

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.

SQL Python dbt or similar transformation frameworks.

Databricks with exposure to Spark Delta Lake and Unity Catalog.

Power BI (primary) with Tableau or Qlik experience transferable.

Lightweight application frameworks such as Streamlit Power Platform or similar.

Exposure to Azure cloud services (ADLS Azure ML Synapse) or equivalent; relational back ends such as Postgres.

Git CI/CD and collaborative development practices.

Data quality and observability tooling such as Great Expectations or Monte Carlo.

MCP RAG frameworks and LLM-powered analytics a plus.

Greystar platforms: GPS for enterprise reporting and Podium for AI enablement and governance.

The salary range for this position is $92000 - $130000 USD Annually.

#LI-BB1

Additional Compensation:

Many factors go into determining employee pay within the posted range including business requirements prior experience current skills and geographical location.

• Corporate Positions:Inaddition to the base salary this role may be eligible to participateina quarterly or annual bonus program based onindividual and company performance.
• Onsite Property Positions:Inaddition to the base salary this role may be eligible to participatein weekly monthly and/or quarterly bonus programs.

Robust Benefits Offered*:

• Competitive Medical Dental Vision and Disability & Lifeinsurance benefits. Low (free basic) employee Medical costs for employee-only coverage; costs discounted after 3 and 5 years of service.
• Generous Paid Time off. All new hires start with 15 days of vacation 4 personal days 10 sick days and 11 paid holidays. Plus your birthday off after 1 year of service! Additional vacation accrued with tenure.
• For onsite team members onsite housing discount at Greystar-managed communities are available subject to discount and unit availability.
• 6-Week Paid Sabbatical after 10 years of service (and every 5 years thereafter).
• 401(k) with Company Match up to 6% of pay after 6 months of service.
• Paid Parental Leave and lifetime Fertility Benefit reimbursement up to $10000 (includes adoption or surrogacy).
• Employee Assistance Program.
• Critical Illness Accident HospitalIndemnity PetInsurance and Legal Plans.
• Charitable giving program and benefits.

*Benefits offered for full-time employees. For Union and Prevailing Wage roles compensation and benefits may vary from the listedinformation above due to Collective Bargaining Agreements and/or local governing authority.

Greystar will consider for employment qualified applicants with arrest and conviction records.

Greystar is an equal opportunity employer and does not discriminate in employment on the basis of race color religion sex (including pregnancy sexual orientation and gender identity) national origin age disability genetic information military or veteran status or any other characteristic protected by applicable law.

Important Notice: Greystar will never request your banking details or other sensitive personal information during the interview process. Greystar does not conduct any interviews via text or messaging and all communication will come from official Greystar email addresses (@). If you receive suspicious requests please report them immediately to