Key skills
AWSETLSparkData EngineeringData LakeGlue
About this role
Role Overview
- Professionals specialized in ETL tools to handle large volumes of data.
- Also responsible for maintaining data pipelines and ensuring secure access to information.
- Responsibilities may also include creating and exposing metrics (dashboards) based on the collected data, thereby ensuring the quality and integrity of these insights.
Requirements
- AWS & Data Engineering
- Building data ingestion and transformation pipelines using AWS services (Glue, Spark)
- Orchestration of jobs and dependencies, failure handling, retries and observability
- Data Quality, Validation and Testing
- Business rule and consistency validations; automated data tests (quality, volume, duplication)
- Monitoring of SLAs and quality indicators
- Modeling and Performance
- Designing and optimizing tables for large data volumes
- Partitioning and distribution strategies
- Cost, performance and processing time optimization
- Best practices for analytical modeling and/or data lake architecture
- Desired: AWS Practitioner and AWS Data Engineer certifications
- Experience with data quality and observability
Tech Stack
- AWS
- ETL
- Spark
Benefits
- Multi-benefit card – choose how and where to use it.
- Scholarship support for undergraduate, postgraduate, MBA and language courses.
- Incentive programs for certifications.
- Flexible working hours.
- Competitive salaries.
- Annual performance review with a structured career plan.
- Opportunity for international career development.
- Wellhub and TotalPass.
- Private pension plan.
- Childcare assistance.
- Medical insurance.
- Dental insurance.
- Life insurance.