Design, implement, test and deliver robust, scalable and reusable data processing and ETL solutions

Automate data extraction, transformation and provisioning, irrespective of type of source and target

Participate in testing activities, create own test cases, manage test data, set up environments

Drive collaborative reviews of design, code, test cases and dataset implementation performed by other data engineers in support of maintaining data engineering standards

Troubleshoot complex data issues and perform root cause analysis to proactively resolve product and operational issues

Mentor and develop other data engineers in adopting best practices

Represent the team as technical ambassador

A minimum of 3 years of experience in Data Software Engineering, showcasing your expertise in designing and developing data engineering solutions

Strong proficiency in Python, PySpark, Databricks, enabling you to build scalable and reliable data pipelines

Solid understanding of data structures and algorithms, enabling you to optimize data processing performance

Good knowledge of Amazon Web Services (AWS) or other cloud platform tools

Experience working with Data warehousing tools, including Dynamo DB, Amazon Redshift, and Snowflake

Average CI/CD knowledge and experience using CI tools like Jenkins

Good testing experience with some of the most common tools in the market (PyTest, Nose, Cucumber, JBehave, etc.)

Fluent spoken and written English at an Upper-Intermediate level or higher, enabling effective communication

Experience in building and deploying machine learning models

Familiarity with Apache Airflow or other workflow management tools