Analytics Engineer - Brazil – Vaga exclusiva para PCD
DoorDash Brazil
| Company | DoorDash Brazil |
| Category | Engineering |
| Location | São Paulo |
| Remote | On-site (inferred) |
| Employment | Not stated |
| Level | Not stated |
| Salary | Not stated by the employer |
| Posted | 29 Mar 2026 |
| Last verified | 10 Aug 2026 |
| Source | Employer ATS (greenhouse) |
Description
Engineering the future of logistics – from Brazil to the world
This position is dedicated to professionals with disabilities (PCD), in accordance with Brazilian Law No. 8.213/91 (Lei de Cotas).
At DoorDash, we believe diverse teams build better products. We are committed to providing an accessible, inclusive workplace with equal opportunities for all.
Reasonable accommodations are available throughout the hiring process and in the work environment. If you require any accommodation, please inform your recruiter at the beginning of the process.
Medical documentation may be requested in accordance with applicable legislation.
DoorDash is building the world’s most reliable on-demand logistics platform. Brazil is a strategic and growing engineering hub for DoorDash. Based in São Paulo, our teams build and scale systems that power millions of users globally. This is an opportunity to shape world-class logistics technology while growing your career in Brazil.This is a unique opportunity to join one of Silicon Valley’s fastest-growing companies, while staying close to home.
Data is at the foundation of DoorDash success. The Data Engineering team builds database solutions for various use cases including reporting, product analytics, marketing optimization and financial reporting. By implementing dashboards, data structures, and data warehouse architecture; this team serves as the foundation for decision-making at DoorDash.
DoorDash is looking for an Analytics Engineer to build and scale data models, pipelines, and self-service analytics across the organization. In this role, you’ll focus on developing a reliable aggregation layer and reporting structure that meets our growing business needs, enabling teams to access and analyze data independently.
You’re excited about this opportunity because you will…
Design, develop, and maintain robust data models to support analytical and product data needs across the organization
Collaborate with data engineers, data scientists, and business stakeholders to understand data requirements and translate them into scalable data solutions
Implement and optimize ETL/ELT processes to ensure data quality, reliability, and performance
Own and define business KPIs, their measurement plans, data requirements and reporting
Build processes to ensure correct, timely and reliable reporting
Address ad-hoc reporting requirements and find pathways for automation
Build and enforce common design patterns to increase report reusability, readability and standardization
Build visually appealing, high-performing, and impactful reporting/dashboard products using tools like Tableau/Sigma across large data sets
**Interviews will be conducted in English**
We’re excited about you because…
3+ years experience working in business intelligence, data analytics, Data engineering or a similar role
Strong SQL skills and experience with data modeling techniques (e.g., dimensional modeling, 3 Nf, data vault)
Proficiency in a programming language such as Python or Scala
Experience building reporting and dashboarding solutions using data lake/Snowflake or similar ecosystem
Expert in Database fundamentals, SQL and performance tuning
Excellent communication skills and experience working with technical and non-technical teams
Comfortable working in fast paced environment, self starter and self organizing
Ability to think strategically, analyze and interpret market and consumer information
Nice to Haves:
Experience with real-time data processing and streaming technologies
Experience with modern data warehousing platforms (e.g., Snowflake, DataBricks, Redshift) and knowledge of data visualization tools (e.g., Looker, Tableau).
Familiarity with machine learning concepts and their data requirements
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