Data Analyst, Mid-level
São Paulo, São Paulo, Brazil
Full Time
3 weeks ago
No Sponsorship
Key skills
AWSAzureCloudETLGoogle Cloud PlatformPythonSQLTableauRData EngineeringData LakeLookerBIPower BIGCPGoogle CloudAgileScrumKanbanCommunication
About this role
Role Overview
- Collect, process, and analyze large volumes of structured and unstructured data
- Develop dashboards and management reports (Power BI, Tableau, or similar)
- Identify patterns, trends, and opportunities for process improvement
- Support strategic decisions with quantitative and qualitative analyses
- Contribute to the development and evolution of business KPIs
- Collaborate with engineering, product, and business teams
- Ensure data quality, governance, and reliability
- Automate data analysis routines and processes
- Participate in defining data architecture and modeling
Requirements
- Proven experience as a Data Analyst (mid-level)
- Advanced SQL skills
- Experience with data visualization tools (Power BI, Tableau, Looker, or similar)
- Knowledge of data modeling and ETL processes
- Experience with Python or R for data analysis
- Experience building KPIs and business metrics
- Ability to translate data into actionable insights
- Good communication skills for interacting with business stakeholders
- Knowledge of data environments (Data Lake, Data Warehouse)
- Preferred: Experience with cloud platforms (GCP, AWS, or Azure)
- Knowledge of data engineering
- Experience with agile methodologies (Scrum/Kanban)
- Previous work on data-driven culture initiatives or data product projects
Tech Stack
- AWS
- Azure
- Cloud
- ETL
- Google Cloud Platform
- Python
- SQL
- Tableau
Benefits
- Collect, process, and analyze large volumes of structured and unstructured data
- Develop dashboards and management reports (Power BI, Tableau, or similar)
- Identify patterns, trends, and opportunities for process improvement
- Support strategic decisions with quantitative and qualitative analyses
- Contribute to the development and evolution of business KPIs
- Collaborate with engineering, product, and business teams
- Ensure data quality, governance, and reliability
- Automate data analysis routines and processes
- Participate in defining data architecture and modeling