Senior Data Engineer – AWS

GFT Technologies

. Organize, collect, and process large volumes of data using ETL tools.

Posted 9/30/2026full-timeRemote • BrazilSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in data management and business intelligence strategies, focusing on ETL processes, AWS tools, and Lakehouse architecture. Proficient in dimensional modeling and ensuring data quality and governance throughout the data lifecycle.

Highest-signal resume keywords

Data Extraction, Transformation, And Loading Using Python/PySparkAWS Cloud: Glue, EMR, Athena, S3Lakehouse Architecture With Apache IcebergDimensional Modeling (Fact And Dimension Tables)AWS Certified Data Engineer

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Applicant Tracking System Keywords

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Hard Skills

ETL ToolsData Pipeline ManagementDimensional ModelingData Quality And ObservabilityInfrastructure-As-Code Tools (Terraform)NoSQL ModelingQuery OptimizationData Lifecycle ManagementPerformance TestingData Governance

Tools & Technologies

AWS Cloud: Lambda, Step Functions, CloudWatchMongoDBDynamoDBHadoopAthenaTrinoPrestoSpark SQLParquetORC

Certifications & Qualifications

AWS Certified Cloud PractitionerAWS Certified Data Engineer

Industry Keywords

Lakehouse ArchitectureData MeshAI Applied To DataData SecurityAccess Control

Tech Stack

Tools & technologies

ApacheAWSCloudDynamoDBETLHadoopMongoDBNoSQLPySparkPythonSparkSQLTerraform

About the role

Key responsibilities & impact

Organize, collect, and process large volumes of data using ETL tools.
Translate the client’s business objectives into an information management and business intelligence strategy.
Maintain data pipelines and ensure secure access to information.
Create and expose metrics and dashboards based on the data collected, ensuring data quality and integrity.
Contribute to data platform modernization initiatives, focusing on Lakehouse architecture, governance, quality, and preparing data for analytics and AI consumption.
Develop dimensional models for use by business teams, dashboards, and reports.
Manage the complete data lifecycle: ingestion, processing, storage, transformation, consumption, and governance.

Requirements

What you’ll need

Senior-level professional specializing in Data/AWS.
Experience with data extraction, transformation, and loading using Python/PySpark ETL tools.
AWS Cloud: Glue, EMR, Athena, SNS, Lambda, Step Functions, S3, Lake Formation, IAM, and CloudWatch.
NoSQL modeling and databases: MongoDB, DynamoDB, and Hadoop.
Lakehouse architecture with Apache Iceberg, including analytical table management, versioning, schema evolution, partitioning, and performance optimization.
Query engines for large-scale data: Athena, Trino, Presto, or Spark SQL, with the ability to optimize queries and work with columnar formats such as Parquet and ORC.
Dimensional modeling (fact and dimension tables, granularity, keys, hierarchies, metrics, and KPIs).
Experience working with structured, semi-structured, and unstructured data.
Execution of unit and performance tests.
Best practices in cloud security, access control, and monitoring.
AWS Certified Cloud Practitioner and AWS Certified Data Engineer certifications.
Experience with data quality and observability.
Experience with infrastructure-as-code tools (Terraform, CloudWatch).
Knowledge of Data Mesh.
Knowledge of AI applied to data.

Benefits

Comp & perks

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