Role Description We are looking for a skilled and motivated Data Engineer Specialist to join our team. The responsibilities of this role is to design, build, and maintain a robust, self-service, scalable, and secure data platform and end-to-end data pipelines that empowers Data Analysts, and Data Scientists to deliver insights and drive strategic decision-making. Responsibilities of a Data Engineer at QuintoAndar: - Build and maintain a high-performance data platform that meets the company's needs, connects with product solutions, and leads analytical innovation, enabling incredible architectures and efficient platforms; - Create and edit data pipelines, considering business logic that best applies to the area in question, choosing levels of aggregation, grouping and transforming fields, checking data quality, and cleaning the data; - Create data modeling and transformation workflows, enabling the creation of clear and accessible data abstractions; - Responsible for the entire code development lifecycle (monitoring deployment, documentation, performance, security, adding metrics and alarms, ensuring SLO budget compliance, and more); - Investigate inconsistencies and be able to trace the source of differences (data troubleshooting); - Enable teams across the company to access and use data more effectively through self-service tools and well-modeled datasets; - Align with stakeholders to understand their primary needs, while also having a holistic view of the problem and proposing extensible, scalable, and incremental solutions; - Conduct PoCs and benchmarks to determine the best tool for a given problem, and decide whether to use an off-the-shelf solution or develop one in-house; - Contribute to defining the strategic vision, crossing team and service boundaries to solve problems; - Advocate for the value of data analytics and engineering within the organization and fostering a data-driven culture; - Be a reference within the chapter on technical concepts, tools, and/or best coding practices. Qualifications - Specialist in technologies, solutions, and concepts of Big Data (Spark, Hadoop, Hive, MapReduce) and multiple languages (YAML, Python); - Experience with Airflow, Spark, AWS and Databricks; - Strong foundation in software engineering principles, with experience working on data-centric systems; - Experience with columnar storage solutions and/or data lakehouse concepts; - Proficiency in Python, or one of the main programming languages, and a passion for writing clean and maintainable code; - Strong knowledge in optimizing SQL query performance; - Experience in building multidimensional data models (Star and/or Snowflake schema); - Understanding of the data lifecycle and concepts such as lineage, governance, privacy, retention, anonymization, etc.; - Knowledge in infrastructure areas such as containers and orchestration (Kubernetes, ECS), CI/CD strategies, infrastructure as code (Terraform), observability (Prometheus, Grafana), among others; - Proficiency in English - our code, documentation, tools, and materials are often structured in English; - Excellent communication skills, proactively sharing and collaborating with both technical and non-technical stakeholders to translate business needs into scalable data solutions; - Experience as a tech/project lead or similar; - Curiosity, detail-orientation, and thrive in a fast-paced, data-driven environment. Requirements - You will stand out if you have participated in building large-scale data platforms for big data sets and teams using Big Data technologies such as Spark, Trino, Hive, Atlas, Ranger, etc.; - Experience in building semantic layers. Benefits - Competitive salary; - Profit sharing; - Meal allowance; - Health insurance; - Dental plan; - Life insurance; - Childcare subsidy and Atypical Parenthood subsidy; - Wellhub; - Home office allowance; - Employee assistance program (mental health, social, legal, and financial support); - Extended parental leave; - Day off on birthday, Mother’s Day, and Father’s Day; - Benefits Club (discounts on everyday services); - Discounts at educational institutions; - Reading kit for children – PlayKids.

• As a Data engineer, you will be responsible for designing, developing and operating our enterprise data platform. • Your role will involve ensuring that data is integrated, governed and AI-ready, thereby creating the backbone for all intelligent applications and insights across the business. • Design and own the enterprise data platform: Build a scalable Microsoft Fabric data infrastructure using a medallion lakehouse architecture (Bronze, Silver or Gold) with Delta Lake. • Develop and operate data pipelines: Create robust batch and streaming ETL/ELT pipelines with schema evolution, automated transformation and strong data quality validation. • Integrate enterprise systems: Connect to data sources such as SharePoint, Salesforce, Microsoft 365, Azure SQL, ERP/CRM systems, file repositories and REST APIs. • Ensure performance and reliability: Optimize storage and processing across structured and unstructured data, including monitoring, alerting, and operational stability. • Build semantic data layers & governance: Establish a taxonomy and metadata standards, as well as semantic models and data cataloguing and lineage tracking. • Enforce access control, compliance and security. • Drive AI readiness: Prepare data for AI use cases through document chunking, embedding pipelines, and vector-ready datasets for RAG. • Expose knowledge services & collaborate: Develop reusable APIs and data services for AI applications and work cross-functionally with AI, analytics, and business teams (#OneTeam).

• Design, build, and optimize end-to-end ETL pipelines for legal data • Work extensively with XML-based legal data feeds: parse, validate, normalize, and transform • Develop and maintain data models and storage schemas • Coordinate data handover and integration from multiple internal and external data providers • Implement and continuously refine metadata enrichment strategies • Build and maintain a high-performance search and retrieval infrastructure • Collaborate with product, AI, and legal domain experts to deliver high-quality data solutions • Own the data integration of one jurisdiction end-to-end

• Lead the most complex and high-impact data architecture initiatives across clients, business areas, domains, platforms, or strategic programs. • Define data architecture strategies that connect business goals, domain structures, semantic logic, platform realities, governance expectations, and downstream consumption needs. • Serve as a senior advisor to internal and client stakeholders on architecture maturity, semantic structure, governance direction, platform-aligned design, and long-term maintainability. • Establish and refine best practices for conceptual, logical, and physical data modeling, business entity design, semantic consistency, metric and dimension alignment, metadata expectations, domain boundaries, and reusable architecture patterns. • Guide the design of scalable semantic structures, business concept frameworks, taxonomy and ontology-informed models, governed access patterns, and reference architectures that improve trust and downstream usability. • Translate ambiguous executive and stakeholder questions into clear architecture approaches, semantic frameworks, platform strategies, governance-aligned design patterns, and business-relevant recommendations. • Lead architecture reviews and design authority discussions to identify risks, resolve ambiguity, strengthen standards alignment, and improve long-term structural quality. • Assess and shape how architecture choices support reporting, analytics, data products, machine learning, AI workflows, retrieval patterns, and agentic systems across structured, semi-structured, and selected unstructured data use cases. • Provide governance direction through standards, design reviews, architecture guardrails, and decision frameworks while keeping hands-on governance execution lighter than framework and review ownership. • Synthesize architecture tradeoffs, semantic implications, platform constraints, and governance considerations into clear insights, strategic implications, and recommended actions. • Influence cross-functional teams across DEPA, DSAI, and AIO to improve how data platforms, governed data products, semantic layers, analytics, and AI workflows work together. • Review major architecture deliverables to ensure quality, clarity, consistency, rigor, and practical business value. • Contribute to thought leadership, growth initiatives, proposal strategy, solution shaping, and new business efforts where senior architecture expertise is required. • Help create, improve, and promote reusable frameworks, templates, standards, semantic models, reference architectures, design review patterns, and accelerators that strengthen delivery consistency across the practice. • Mentor senior practitioners and help define what excellent data architecture practice looks like across the organization. • Reinforce strong governance, privacy, security, and data-quality expectations across engagements and teams.