Data Engineer

Ingestion & Transformation

• Build and maintain scalable data pipelines using Python, dbt, and Snowflake-native tooling (Tasks/Streams), deployed on AWS (S3, Airflow, ECS).
• Assist in migrating existing Dataiku pipelines into Snowflake to simplify architecture and improve efficiency.
• Continuously improve workflows for performance, reliability, and maintainability.

Semantic Modeling & Views

• Develop and maintain Snowflake semantic views that support analytics and reporting needs.
• Apply data modeling best practices to ensure consistency across business domains.
• Support creation of governed, role-based semantic layers for Finance, Operations, and other enterprise areas.

Governance & Security

• Implement data access controls aligned with enterprise RBAC frameworks.
• Support metadata management and cataloging for visibility in Sigma.
• Ensure pipelines and models meet enterprise data governance standards.

Analytics Enablement

• Deliver clean, governed data sets for Sigma dashboards and embedded analytics use cases.
• Monitor and optimize query performance to balance cost and speed.
• Partner with analysts and BI developers to ensure data is reliable and ready for consumption.

Continuous Improvement

• Stay current with Snowflake and Sigma feature updates.
• Recommend improvements to data engineering processes and automation where possible.
• Contribute to a culture of learning and collaboration within the CoE.

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field.

• At least 5 years of experience as a Data Engineer or in a similar role., * Snowflake expertise: SQL, Tasks/Streams, and performance
• Cloud experience: Hands-on with AWS (S3, VPC, IAM, Airflow, ECS; Lambda a plus).
• Data modeling: Familiar with dimensional modeling, database design, and semantic layer design.
• ETL/ELT engineering: Experience building and optimizing data pipelines, including hands-on work with dbt for transformations.
• Governance & Security: Understanding of AD/LDAP integration, RBAC, and data cataloging.
• Programming: Proficient in SQL and Python.
• AI fluency: Demonstrated experience using AI tools and LLMs in day-to-day engineering work (prompting, code generation, workflow automation). Candidates should expect this to be a major focus of the interview.
• Strong problem-solving skills and a desire to learn emerging data technologies.

Preferred Skills

• Experience supporting Power BI to Sigma migrations or similar modern BI transitions.