Key skills
AzureETLIoTPythonScalaSQLVaultAIMLMLOpsELTData EngineeringData LakeSnowflakeDatabricksdbtGitSaaSCI/CDLeadershipCollaboration
About this role
Role Overview
- Design, build and evolve Data Lake / Data Lakehouse architectures, ensuring scalability, resilience and performance;
- Define the reference architecture for the data platform within the Microsoft Azure ecosystem, with strategic use of Microsoft Fabric, Azure Purview and Azure AI Foundry;
- Evaluate and recommend technologies, tools and standards to compose the company's data stack;
- Ensure adoption of best practices for security, partitioning, versioning and data organization (Bronze, Silver, Gold / Medallion Architecture);
- Plan and oversee the construction of ELT/ETL pipelines for data ingestion, transformation and delivery;
- Define strategies for data integration across multiple sources (legacy systems, APIs, relational and non-relational databases, SaaS, IoT, etc.);
- Implement and promote the use of pipeline acceleration frameworks, such as Databricks and/or Snowflake, to increase productivity and standardization;
- Ensure observability and monitoring of pipelines through DataOps practices;
- Define and apply advanced data modeling standards (dimensional, relational, Data Vault 2.0, etc.) to support various analytical and operational contexts;
- Establish and monitor data quality metrics (completeness, consistency, accuracy and timeliness);
- Promote standardization of data contracts between producer and consumer teams;
- Define and implement the organization's data governance framework, including cataloging, classification, lineage and access policies;
- Act as a technical reference for the adoption of Azure Purview for metadata management and compliance;
- Ensure compliance with regulations such as LGPD/GDPR, defining privacy and data security controls from design (Privacy by Design);
- Collaborate with AI teams on data preparation and building ML pipelines;
- Support adoption of Azure AI Foundry to structure data flows aimed at AI models and LLMs;
- Promote MLOps and feature engineering best practices at the data layer;
- Lead, mentor and develop the Data Engineering team, fostering a culture of quality, collaboration and continuous learning;
- Conduct technical ceremonies such as code reviews, design reviews and architecture decision records (ADRs);
- Translate business needs into technical architecture requirements, acting as a bridge between technical and executive teams.
Requirements
- Strong experience in Data Architecture and Data Engineering;
- Expertise in Data Lake, Lakehouse and Data Warehouse architectures in cloud environments;
- Advanced experience with Microsoft Azure (Data Factory, Synapse, ADLS Gen2, Event Hub, etc.);
- Experience with Microsoft Fabric and/or Azure Purview;
- Advanced knowledge of data modeling (dimensional, relational, Data Vault);
- Experience building large-scale ELT/ETL pipelines;
- Knowledge of data governance and frameworks such as DAMA-DMBOK;
- Technical leadership skills and ability to communicate with stakeholders at different levels;
- Experience with advanced SQL and programming languages such as Python or Scala;
- Familiarity with DataOps practices, data CI/CD and versioning (e.g., dbt, Git).
Tech Stack
- Azure
- ETL
- IoT
- Python
- Scala
- SQL
- Vault
Benefits
- Health and dental insurance;
- Meal and food allowances;
- Childcare assistance;
- Extended parental leave;
- Partnerships with gyms and health & wellness professionals via Wellhub (Gympass) TotalPass;
- Profit Sharing (PLR);
- Life insurance;
- Continuous learning platform (CI&T University);
- Employee discount club;
- Free online platform dedicated to physical and mental health and well-being;
- Prenatal and responsible parenthood course;
- Partnerships with online course platforms;
- Language learning platform;
- And many more