Senior Data Engineer leading the design and implementation of data pipelines for NVIDIA’s analytics and monitoring systems. Collaborating across teams to enhance data ingestion and analysis capabilities.
About the role
- Senior Data Engineer at Red Hat designing and optimizing 데이터 솔루션 supporting sales and forecasting. Collaborating with teams and applying modern data engineering practices to ensure data quality.
Responsibilities
- Design, build, and optimize scalable data solutions supporting sales, renewals, incentives, and forecasting
- Develop and optimize complex SQL queries across large, multi-source datasets to support analytics and reporting
- Build and maintain well-structured analytical models using modern ELT patterns
- Use Python to cleanse, reconcile, and transform raw datasets
- Build, schedule, and monitor data pipelines using orchestration tools like Airflow
- Develop and maintain transformation logic using tools like dbt
- Leverage AI-assisted tools to accelerate SQL development and testing
- Implement automated data quality checks and participate in validation audits
- Partner with analysts and business stakeholders to understand requirements
- Contribute to version-controlled repositories and follow CI/CD practices
- Write unit and integration tests to validate transformations
Requirements
- 6–9 years of experience as a Data Engineer, BI Engineer, or Analytics Engineer
- Bachelor’s or Master’s degree in Computer Science, Engineering, Information Systems, or equivalent practical experience
- Strong SQL Skills: Proven expertise in writing and optimizing complex SQL in relational or cloud data warehouse environments
- Python Proficiency: Hands-on experience using Python for data transformation, automation, and validation
- Modern Data Stack: Experience with Snowflake, dbt, or similar modern cloud-based data platforms
- ETL / ELT Tools: Familiarity with tools such as Fivetran or comparable data ingestion frameworks
- Data Quality Mindset: Experience implementing automated data checks, reconciliations, and validation workflows
- Execution-Focused Ownership: Able to independently deliver high-quality data solutions within defined problem spaces
- Analytical Thinking: Strong problem-solving skills with the ability to debug complex data and pipeline issues
- Learning Agility: Comfortable adopting new tools, AI-assisted workflows, and modern engineering practices
- Collaboration: Effective communicator who works well with cross-functional and globally distributed teams
- Adaptability: Thrives in a fast-paced environment with evolving priorities and multiple concurrent deliverables.
Benefits
- Flexible working hours
- Professional development opportunities
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