Design and implement scalable ETL pipelines in a cloud-native AWS environment

Develop and maintain data processing workflows using Python, Spark, and Airflow

Optimize data storage solutions and implement advanced data management strategies using AWS and Redshift

Ensure the platform meets non-functional requirements by defining robust testing strategies and execution plans

Collaborate with cross-functional teams to create and maintain decision and change logs, ensuring consistent communication and tracking of technical decisions

Proactively identify opportunities to enhance the quality of the product while managing scope effectively

Monitor and fine-tune data pipelines for performance, scalability, and maintainability

Provide technical guidance and mentorship to team members, promoting best practices in data engineering

Troubleshoot and resolve complex issues in data workflows and pipelines

3+ years of experience working in Big Data engineering roles

Strong data engineering and overall engineering skills, including expertise in testing and quality assurance

Proficiency in Python

Background in Spark, Airflow, and AWS technologies like Redshift

Proven experience in decision-making processes with the ability to document and maintain decision and change logs

Understanding of strategies to meet non-functional requirements such as performance, scalability, and testing

Capability to design solutions that enhance product quality without significantly increasing scope

Excellent command of English (B2+ level), both written and spoken

Knowledge of Ruby, Iceberg, Trino, Cassandra, and Tableau