Senior Data Analyst II

GFT Technologies

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

Posted 10/2/2026full-timeRemote • BrazilSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in data management and business intelligence strategies, with a strong focus on ETL processes, data pipeline maintenance, and Lakehouse architecture. Proficient in utilizing AWS tools and ensuring data quality and security throughout the data lifecycle.

Highest-signal resume keywords

ETL ToolsPythonAWS GlueLakehouse ArchitectureDimensional Modeling

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data ExtractionData TransformationData LoadingNoSQL ModelingQuery EnginesData QualityPerformance TestingCloud SecurityInfrastructure-as-CodeAI Applied to Data

Tools & Technologies

AWS EMRAWS AthenaAWS LambdaAWS S3AWS CloudWatchMongoDBDynamoDBHadoopApache IcebergTerraform

Certifications & Qualifications

AWS Certified Cloud PractitionerAWS Certified Data Engineer

Industry Keywords

Data PipelineData GovernanceData MeshData ObservabilityColumnar FormatsStructured DataSemi-Structured DataUnstructured DataData LifecycleBusiness Intelligence

Tech Stack

Tools & technologies

ApacheAWSDynamoDBETLHadoopMongoDBNoSQLPySparkPythonSparkSQLTerraform

About the role

Key responsibilities & impact

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

Requirements

What you’ll need

Senior-level professional working with Data/AWS.
Experience in data extraction, transformation, and loading using ETL tools, Python, and PySpark.
Experience with AWS Glue, EMR, Athena, SNS, Lambda, Step Functions, S3, Lake Formation, IAM, and CloudWatch.
Knowledge of NoSQL modeling and databases, including MongoDB, DynamoDB, and Hadoop.
Experience with Lakehouse architecture using Apache Iceberg.
Experience with query engines for large data volumes, such as Athena, Trino, Presto, or Spark SQL.
Knowledge of the Parquet and ORC columnar formats.
Knowledge of dimensional modeling.
Experience working with structured, semi-structured, and unstructured data.
Experience performing unit and performance testing.
Knowledge of cloud security best practices, access control, and monitoring.
AWS Certified Cloud Practitioner and AWS Certified Data Engineer certifications are a plus.
Experience with data quality and observability is a plus.
Experience with infrastructure-as-code tools, such as Terraform and CloudWatch, is a plus.
Knowledge of Data Mesh is a plus.
Knowledge of AI applied to data is a plus.

Benefits

Comp & perks

Multi-benefit card – choose how and where to use it.
Education scholarships 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.