Tech Stack
AirflowAWSAzureCloudPySparkScalaSparkSQLUnityVault
About the role
- Design, build and maintain scalable data pipelines (batch and streaming);
- Lead data modeling and organization in Lakehouse environments;
- Ensure data quality, performance and governance in production;
- Implement and maintain Data Lakes / Data Warehouses in the cloud (e.g., Delta Lake, Snowflake, S3);
- Automate processes for data ingestion, transformation and loading;
- Work closely with data scientists, analysts and software engineers;
- Apply DataOps / MLOps best practices and data versioning;
- Serve as technical lead in defining a modern and secure data architecture.
Requirements
- Solid experience in data engineering (minimum 6 years);
- Proficiency with Spark (PySpark or Scala) in distributed environments;
- Experience with Databricks and Delta Lake;
- Advanced SQL and data modeling knowledge (Kimball, Data Vault, etc.);
- Strong experience with cloud platforms (Azure or AWS), especially data services;
- Knowledge of pipeline orchestration (e.g., Airflow, ADF, Databricks Jobs);
- Familiarity with CI/CD, code versioning (Git) and software engineering best practices;
- Experience with cloud data storage (Blob Storage, S3, GCS);
- Understanding of data security and governance (e.g., Unity Catalog, IAM, RBAC).
- Health insurance;
- Dental insurance;
- Meal allowance;
- Gympass;
- Home office allowance;
- PPR - Profit Sharing Program;
- Private pension;
- Group life insurance;
- Educational partnerships;
- Discounts at Riachuelo.
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
data engineeringSparkPySparkScalaSQLdata modelingDataOpsMLOpspipeline orchestrationcloud data storage
Soft skills
leadershipcollaborationcommunication