Data Engineer

Minimum qualifications:

• Bachelors degree or equivalent practical experience.
• 4 years of experience in a data engineering, data infrastructure, or data analytics role.
• Experience with database administration techniques or data engineering, as well as writing software in Java, C++, Python, Go, or JavaScript.

Preferred qualifications:

• Master’s degree in a relevant field.
• 5 years of experience in a data engineering, data infrastructure, or data analytics role.
• Experience delivering and maintaining complex data projects from conception to production.

About the job:

As a Data Engineer, you will take a significant role in designing and building the next generation of our data infrastructure. You will be responsible for architecting, implementing, and optimizing complex and scalable data pipelines, moving beyond basic development to own key components of our data warehouse. This role requires a strong technical expert who can manage massive datasets, write highly efficient SQL and Python code, and collaborate effectively with senior stakeholders and other engineers. You will not only build innovative data foundations, and AI-driven insights solutions, but also help define the standards and best practices that elevate the entire team, driving data quality and AI-readiness initiatives.

Responsibilities:

• Design, build, and maintain scalable data pipelines to ingest, process, and store data. Implement robust quality checks and monitoring to ensure data accuracy and reliability.
• Write complex SQL queries for extraction, transformation, ad-hoc analysis, and automated reporting. Develop scalable data foundations and models designed to support AI/ML initiatives.
• Develop, test, and deploy intelligent agents using Python and the Google ADK framework to automate tasks like data analysis, report generation, and system orchestration.
• Partner with senior stakeholders, data scientists, and AI teams to understand complex requirements and architect robust long-term data solutions.
• Develop tools to automate data processes, facilitate faster turnarounds, and increase efficiency. Monitor, troubleshoot, and tune data systems and pipelines.

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