Data Engineer

About the Company

Our client is a global enterprise cloud leader trusted by thousands of organizations worldwide to manage their most critical business operations, human capital, and financial systems. Known for continuous innovation and human-centric design, they process millions of complex transactions every day with a focus on reliability and security. By joining their engineering organization, you will contribute directly to large-scale platforms that power how Fortune 500 companies run their everyday operations.

Why You’ll Love This Role

Are you looking to move beyond maintaining legacy, brittle pipelines and instead build enterprise-grade data platforms using the best modern tools available?

As a Data Engineer on the Data Management Engineering team, you will design, scale, and own the data foundation that drives critical business decisions. You will work extensively with Snowflake, dbt, and emerging AI technologies, crafting automated pipelines, robust governance frameworks, and high-performance data products from day one.

Key Responsibilities

• Architect & Build: Design, deploy, and scale robust, automated data pipelines and architectures across the enterprise data ecosystem.

• Modern Data Modeling: Leverage Snowflake and dbt to develop clean, performant, and reliable dimensional models and production data assets.

• Data Reliability & Governance: Implement automated data quality frameworks, robust monitoring, and governance practices to ensure data assets are secure, durable, and performant.

• Modern CI/CD Practices: Maintain engineering rigor with Git-based version control, CI/CD deployment pipelines, and modern orchestration tools.

• AI Integration: Explore and integrate AI/ML capabilities into data management workflows to automate operations and unlock new product capabilities.

Who We Are Looking For

• Experience: 3+ years of professional hands-on data engineering experience.

• Education: Bachelor’s degree in Computer Science, Data Science, Engineering, or a related technical discipline.

• Core Stack: Advanced SQL and solid production programming skills in Python.

• Modern Tools: Proven production experience modeling data using Snowflake and dbt.

• Engineering Standards: Solid grasp of modern orchestration, CI/CD pipelines, Git workflows, and automated testing/data quality suites.

• Bonus Points: Hands-on exposure to cloud infrastructure (AWS/GCP/Azure) or experience incorporating AI tools and workflows into data pipelines.