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Grupo QuintoAndar | Data Intelligence Specialist (Collections & New Businesses )

QuintoAndar São Paulo, São Paulo, BrazilEst. Est. BRL 72,000–108,000 / monthSenior

Estimated range based on role, country and industry — not published by the company.

Key requirements

  • Python
  • Sql
  • Airflow
  • Dbt
About Grupo QuintoAndar We are Grupo QuintoAndar, the largest housing platform in Latin America . Driven by our shared purpose of helping people love where they live , we build technology and solutions that simplify the entire housing journey, from renting to buying and selling. Operating across 6 countries (Brazil, Mexico, Argentina, Peru, Ecuador, and Panama) and with a technology hub in Portugal , we bring together more than 3,100 people united by the challenge of transforming the real estate industry. Backed by top global investors such as SoftBank, General Atlantic, and Kaszek, we have raised over US$ 755 million and are valued at US$ 5.1 billion . We lead market transformation as pioneers in large-scale Artificial Intelligence applications for the real estate sector. Our platforms welcome over 50 million monthly visits, managing more than 350,000 active rental contracts and processing R$ 20 billion in annual transaction value. If you are looking to solve complex problems with autonomy, cutting-edge technology, and massive scale impact, come on in, the house is yours. To learn more about our story, visit: https://grupoquintoandar.com/ Location & Work Model Our team works in a hybrid model, combining remote work flexibility with in-person some days a month. About the Team & The Challenge The Specialist is the analytics backbone of the New Business collections pod. As each new initiative is stood up, this person builds the data foundation it runs on — tables, pipelines, dashboards, and reporting routines — so that collections performance is measurable from the first day the operation goes live. The core of the role sits in construction: modeling the underlying data layer, translating the pod's indicator framework into reliable metric logic, and building the reporting infrastructure through which each new initiative is monitored. Everything the pod concludes about performance rests on assets this person owns. Alongside that, the Specialist is expected to read the numbers critically — validating that outputs make sense and surfacing anomalies and data-driven observations that inform the team's analysis. This is a hands-on, high-ownership position with direct visibility into how new businesses are built and managed. Your Responsibilities Data foundation Model and build the data layer that supports collections in each new initiative — tables, entities, events, and aggregations — in partnership with BI & Data and Engineering. Map available source data for each new product or vertical, assess gaps, and specify what must be instrumented for collections to be measurable. Ensure data quality, consistency, and reliability across the pod's assets, including validation routines and documentation. Maintain and evolve the pod's data assets as initiatives scale and requirements change. Dashboards and reporting infrastructure Build and maintain the dashboards through which each new initiative's collections performance is monitored. Structure the recurring reporting routines that feed the pod's management rituals and the initiative leadership's forums. Implement the pod's indicator framework in the data layer — metric logic, calculation rules, granularity, and refresh cadence — ensuring definitions are consistent, documented, and auditable across initiatives. Automate recurring outputs wherever possible, reducing manual effort as the portfolio of initiatives grows. Analytical support Support the pod's performance analysis with the data cuts, queries, and ad hoc analyses required to investigate questions as they arise. Validate that reported figures hold up, and surface anomalies, inconsistencies, or unexpected patterns in the data. Contribute to defining what should be measured in each new initiative, bringing the technical feasibility perspective early in the design. Translate technical work and data findings into clear terms for business and

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