Analytics Engineer

Overview In this Analytics Engineer role within Greystar’s Decision Intelligence pod, you will turn a data foundation into usable data products that support daily decisions. You’ll work closely with business teams to understand goals, build end-to-end data solutions, and ship dashboards, models, or lightweight apps. AI is central to the work, enabling faster, smarter decision making across real estate operations. You’ll own initiatives from question to delivery and help scale solutions across the company with governance and trust at the core. Compensation / BenefitsCompetitive medical, dental, vision, and disability & life insuranceGenerous paid time off including vacation, personal days, sick days, and holidays6-week paid sabbatical after 10 years of service401(k) with company matchPaid parental leave and fertility benefitsOnsite housing discount for eligible onsite staff ResponsibilitiesOwn initiatives end-to-end from business question to deployed data productEmbed in a business area to learn goals, data, and workflowsProactively recommend improvements based on deeper business understandingShip data products quickly (dashboards, models, lightweight apps) and iterate with usersBuild/maintain data models in Databricks and SQL with strong data integrityDesign experiments to prove impact and distinguish correlation vs. causationPresent findings and recommendations to varied stakeholdersIdentify opportunities to graduate products to shared platforms (GPS, Podium) and collaborate with platform teamsContribute reusable patterns, tooling, and documentationUse AI tools to build faster and design smarter solutionsValidate data quality and implement monitoring, testing, and governanceDocument limitations and caveats for users Key requirements3+ years in a high-performing analytics or data team with end-to-end ownershipQuantitative field background or equivalent practical experienceAdvanced SQL and data modeling with grain/keys and referential integrityStrong Python skills for data analysis and toolingExperience with modern lakehouse/warehouse platforms (Databricks preferred)Exposure to ML techniques (classification, clustering, prediction, A/B testing)Fluency with BI tools (Power BI required; Tableau/Qlik transferable)Experience with AI tooling (LLM coding tools) and data product reliability in AI-enabled contextsKnowledge of data governance, provenance, and privacy considerationsExcellent communication and ability to tailor messaging to diverse audiencesSelf-directed, collaborative, and capable of owning problems end-to-endSelf-directionResourcefulnessBias toward actionSQLPythonDatabricks (Spark, Delta Lake, Unity Catalog)