,Remote

• Data Architecture and Integration - Integrate diverse data sources by designing and implementing scalable high performance data pipelines and infrastructure.

o Build ETL processes using a variety of methods to facilitate efficient movement of data between systems

o 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.

o Use DBT or Snowflake native capabilities to transform data and ensure data is accurate and up to date.

o 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.

• Experience: Significant experience (usually 5+ years) in data engineering roles, with a focus on designing and implementing scalable data solutions.

• Programming Skills: Proficiency in programming languages such as Python, Java, or Scala.

• Database Knowledge: Expertise in working with both SQL and NoSQL databases, with hands-on experience in database design and optimization. Proficiency in Snowflake is required.

• Knowledgeable of Machine Learning concepts and an interest to grow in the area.

• Cloud Platforms: Experience with cloud platforms like Azure, AWS, or Google Cloud Platform, and proficiency in leveraging cloud-based.

• Version Control: Knowledge of version control systems like Git or Azure DevOps for managing the codebase.

• Problem-Solving Skills: Strong analytical and problem-solving skills to address complex data engineering challenges specific to educational data.

• Communication: Excellent communication skills to effectively collaborate with cross-functional teams and non-technical stakeholders.

• Adaptability: Ability to adapt to evolving technologies and industry best practices in the field of data engineering.

Nice to Have:

• Experience using TIBCO EBX

• Experience using Boomi or other low code ETL tools

• Experience using Azure for Data Engineering tasks i.e. function apps and other Azure functionality that can be used to build data pipelines.