Design, develop, and maintain data transformation processes to cleanse, transform, and aggregate data within the data warehouse.
Optimize and streamline data workflows to ensure efficient data processing and storage in the data warehouse.
Collaborate with data analysts, data scientists, and business stakeholders to understand data requirements and implement appropriate data transformations.
Develop and maintain data warehouse schemas, tables, and views to support reporting and analytics needs.
Implement data governance policies and standards to ensure data quality, security, and compliance.
Monitor data warehouse performance, troubleshoot issues, and optimize for scalability and efficiency
Requirements
Bachelor's degree in Computer Science, Informatics Engineering, or a related field with data processing.
2+ years of professional experience as a Data Engineer or similar role, with a focus on data warehousing, data pipelining, modeling, and transformation.
Strong Proficiency in programming languages: Python and SQL
Experience with application orchestration Docker and Kubernetes
Experience with API integration techniques and tools for data extraction, including RESTful APIs, JSON, and OAuth.
Have strong understanding and familiar with data pipeline, data modeling, Kimball data warehouse concept, star schema, and ETL processes.
Experience with cloud platforms such as AWS, Google Cloud Platform for deploying and managing data pipelines
Experience with cloud data warehouse such as: Google BigQuery and Snowflake
Ability to optimize data pipelines and query performance in a large-scale data warehouse.
Excellent analytical and problem-solving skills, with attention to detail and data accuracy.
Strong communication skills to work effectively in cross-functional teams and present data solutions to stakeholders.
