Grupo QuintoAndar | Staff Data Engineer

About the role

The role centers on transforming raw information into strategic decisions within a remote-first environment in Brazil, with the option for an office presence in São Paulo. You will own the design, construction, and maintenance of the data pipelines that power critical business operations and analytics. This position requires a strong blend of statistics, coding, and communication to bridge the gap between technical systems and business teams. You will work directly with business stakeholders to ensure that the data infrastructure supports scalable and reliable decision-making. Nearly every modern company, from startups to banks, relies on robust data teams to function effectively. A strong portfolio demonstrating past analyses and data systems will be a key factor in evaluation. You will be responsible for turning complex requirements into resilient data products that serve the entire organization.

Key facts

What you'll do

• Design and implement scalable data pipelines using Big Data technologies such as Spark, Hadoop, Hive, and MapReduce.
• Develop and maintain data infrastructure using programming languages such as YAML and Python to ensure reliability and performance.
• Configure and optimize workflows in Airflow, Spark, AWS, and Databricks to support efficient data processing.
• Build and manage columnar storage solutions and apply data lakehouse concepts to organize structured and unstructured data.
• Write clean, maintainable, and documented code in Python or other main programming languages for production environments.
• Analyze and optimize SQL query performance to ensure fast and efficient data retrieval across large datasets.
• Model data using multidimensional approaches such as Star and Snowflake schemas to support analytical workloads.
• Oversee the data lifecycle by implementing strategies for lineage, governance, privacy, retention, and anonymization.
• Manage infrastructure through containers and orchestration tools such as Kubernetes and ECS, applying CI/CD strategies and infrastructure as code with Terraform.
• Ensure system observability by integrating monitoring tools like Prometheus and Grafana for proactive issue detection.
• Communicate fluently in English to interpret global documentation, tools, and technical materials used in the development process.
• Collaborate with both technical and non-technical stakeholders to translate business needs into scalable data solutions.
• Act as a technical or project lead by taking ownership of complex initiatives and guiding cross-team data strategies.
• Thrive in a fast-paced, data-driven environment by demonstrating intense curiosity and meticulous attention to detail.
• Contribute to the development of large-scale data platforms that serve big data sets and large teams using modern technologies.

Requirements

• Hold a bachelor's degree as a minimum academic requirement for this position.
• Demonstrate specialization in Big Data technologies and concepts such as Spark, Hadoop, Hive, and MapReduce.
• Show proficiency in multiple programming languages, including YAML and Python, for building data solutions.
• Provide evidence of experience with Airflow, Spark, AWS, and Databricks in production or large-scale projects.
• Maintain a strong foundation in software engineering principles and data-centric system design.
• Work effectively with columnar storage solutions and understand data lakehouse architectures and best practices.
• Write clean, maintainable code in Python or another main programming language that adheres to industry standards.
• Apply advanced knowledge to optimize SQL query performance across complex and large datasets.
• Model data using multidimensional approaches such as Star and Snowflake schemas to support business intelligence.
• Demonstrate a thorough understanding of the data lifecycle and related concepts including lineage, governance, privacy, retention, and anonymization.
• Manage infrastructure using containers and orchestration tools such as Kubernetes and ECS, along with CI/CD strategies and infrastructure as code with Terraform.
• Communicate fluently in English, as code, documentation, tools, and materials are predominantly in English.
• Communicate effectively with both technical and non-technical stakeholders to ensure alignment on objectives and outcomes.
• Accept responsibility as a tech or project lead or similar role, driving initiatives from conception to execution.
• Thrive with curiosity and detail-orientation in a fast-paced, data-driven environment that demands high-quality output.

Nice to have

• Contribute to building large-scale data platforms for big data sets and teams using technologies such as Spark, Trino, Hive, Atlas, and Ranger.
• Gain experience building semantic layers that enable consistent and efficient data consumption across the organization.

Practical notes

• The hiring process begins with the application; all applicants receive individual analysis and feedback.
• The selection process stages include application, recruiter interview, tech screening, technical interviews with the data team, and offer evaluation.
• Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

Data platforms and pipelines power modern analytics across large user bases. Tools such as Spark, Hive, Airflow, AWS, and Databricks support scalable, reliable solutions. Data modeling, infrastructure as code, and observability practices support maintainable, secure systems. Cross-team collaboration and a data-driven culture guide decision-making and product impact.

Questions to ask

Worth asking in any interview: how the team measures success, who the role works with daily, what the onboarding looks like, and what the company is trying to achieve this year. Asking what past hires did well is a strong final question. Keep the list short and pick the questions that matter most to you.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.