Senior Data Engineer – AWS

• Organize, collect, and process large volumes of data using ETL tools.
• Translate the client’s business objectives into information management and business intelligence strategies.
• Develop and maintain data pipelines, ensuring secure access to information.
• Create and deliver metrics and dashboards based on collected data, ensuring information quality and integrity.
• Contribute to the modernization of the data platform, focusing on Lakehouse architecture, governance, quality, and preparing data for analytical and AI consumption.
• Design dimensional models for use by business teams, dashboards, and reports.
• Work across the entire data lifecycle, including ingestion, processing, storage, transformation, consumption, and governance.
• Develop and optimize pipelines using Python, PySpark, and AWS services.
• Work with Apache Iceberg, contributing to analytical table management, versioning, schema evolution, partitioning, and performance optimization.
• Use query engines for large volumes of data, such as Athena, Trino, Presto, or Spark SQL, working with columnar formats such as Parquet and ORC.
• Implement unit and performance tests, along with security best practices, access control, and cloud monitoring.
• Contribute to the quality, traceability, integrity, and efficiency of data processes.

• Experience collecting, transforming, and loading data using ETL tools.
• Experience with Python and PySpark.
• Experience with AWS services, including Glue, EMR, Athena, SNS, Lambda, Step Functions, S3, Lake Formation, IAM, and CloudWatch.
• Knowledge of NoSQL databases, such as MongoDB and DynamoDB, and related Hadoop technologies.
• Experience with Lakehouse architecture and Apache Iceberg, including analytical table management, versioning, schema evolution, partitioning, and performance optimization.
• Experience with query engines for large volumes of data, such as Athena, Trino, Presto, or Spark SQL.
• Knowledge of columnar formats, such as Parquet and ORC.
• Experience with dimensional modeling, including facts, dimensions, granularity, keys, hierarchies, metrics, and KPIs.
• Experience processing structured, semi-structured, and unstructured data.
• Knowledge of conducting unit and performance tests.
• Experience applying security best practices, access control, and monitoring in cloud environments.
• AWS Cloud Practitioner and AWS Data Engineer certifications are a plus.
• Experience with Data Quality and Data Observability is a plus.
• Knowledge of infrastructure as code, such as Terraform, and monitoring tools such as CloudWatch is a plus.
• Knowledge of Data Mesh is a plus.
• Knowledge of artificial intelligence applied to data is a plus.
• Problem-solving skills.
• Ability to work independently and manage your own time.

• Multi-benefit card – choose how and where to use it.
• Scholarships for undergraduate, graduate, MBA, and language courses.
• Certification incentive programs.
• Flexible working hours.
• Competitive salaries.
• Annual performance review with a structured career development plan.
• International career opportunities.
• Wellhub and TotalPass.
• Private pension plan.
• Childcare assistance.
• Medical insurance.
• Dental insurance.
• Life insurance.