Design and implement data pipelines, data warehousing and ETL processes

Collaborate with cross-functional teams to understand data needs and deliver effective solutions

Ensure data quality and accuracy by developing and implementing testing and validation processes

Optimize data solutions for performance, scalability and reliability

Create and maintain documentation for data solutions, including data models, schemas and processes

Provide technical guidance and support to junior team members

Stay up-to-date with emerging trends and technologies in Data Software Engineering

At least 3 years of experience in Data Enginering with sound knowledge of Apache Spark

Strong proficiency in AWS, including experience with EC2, S3, RDS and Redshift

In-depth knowledge of Snowflake, including experience with data modeling and performance optimization

Experience with Apache Airflow, including creating and managing DAGs and working with operators and sensors

Strong SQL development skills, including experience with complex queries, stored procedures and optimization

Excellent communication skills and strong critical thinking capabilities to effectively convey feedback and insights

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

Experience with other cloud-based data solutions, such as Google Cloud Platform or Microsoft Azure

Experience with data visualization tools, such as Tableau or Power BI