Data Architecture and Integration - Integrate diverse data sources by designing and implementing scalable high performance data pipelines and infrastructure.
Build ETL processes using a variety of methods to facilitate efficient movement of data between systems
Incorporate best practices to ensure data pipelines are robust and include error handling and alerting.
Data Model: Design data models that turn complex organizational data into easy to use data structures ensuring seamless flow for Client's operational, analytics and reporting needs.
Use DBT or Snowflake native capabilities to transform data and ensure data is accurate and up to date.
Optimize data structures and queries to ensure efficient data storage and retrieval.
Data Quality: Implement and enforce data quality standards to ensure the accuracy and reliability of educational data used in academic assessments globally.
Collaboration: Collaborate with cross-functional teams, including analysts and business stakeholders, to understand unique data requirements for data processing or analytics needs.
Security: Implement and maintain data security measures to protect sensitive information, ensuring compliance with international data privacy regulations.
Snowflake Expertise: Possess expertise in Snowflake for effective data warehousing, transformations, data sharing, and structured management in a cloud-based environment.
Documentation: Create and maintain comprehensive documentation for data engineering processes, systems, and workflows
Team Leadership: Provide technical leadership and mentorship to junior members of the data engineering team, fostering a collaborative environment dedicated to excellence in handling educational data.
Tableau: Utilize Tableau to deliver data insights using impactful visualizations in a meaningful and actionable manner.
