Data Engineer
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Job Location:
Dallas, IA - USA
Monthly Salary:
Not Disclosed
Posted on:
15 hours ago
Vacancies:
1 Vacancy
Job Summary
Job Description:
Data Engineering & Pipelines
- Design build and maintain scalable data pipelines using Python SQL PySpark and Microsoft Fabric.
- Integrate with external systems using REST/Graph APIs managing authentication pagination rate limits and error handling.
- Support and enhance existing pipelines ensuring high reliability observability and performance.
- Migrate current Python-based or legacy pipelines into Microsoft Fabric (Data Factory Data Engineering/Notebooks Lakehouse).
Data Analytics & Modeling
- Build and maintain data models semantic layers and analytics-ready datasets.
- Develop transformations and calculations needed for BI and reporting.
- Create or support analytical outputs using tools such as Power BI.
- Implement data quality checks validation rules and data governance best practices.
Operations & Continuous Improvement
- Troubleshoot pipeline/data issues identify root causes and provide long-term solutions.
- Optimize performance cost and reliability across all data workflows.
- Collaborate with business product analytics and IT teams to translate requirements into technical solutions.
- Maintain documentation data lineage and change management practices.
Data Engineering & Pipelines
- Design build and maintain scalable data pipelines using Python SQL PySpark and Microsoft Fabric.
- Integrate with external systems using REST/Graph APIs managing authentication pagination rate limits and error handling.
- Support and enhance existing pipelines ensuring high reliability observability and performance.
- Migrate current Python-based or legacy pipelines into Microsoft Fabric (Data Factory Data Engineering/Notebooks Lakehouse).
Data Analytics & Modeling
- Build and maintain data models semantic layers and analytics-ready datasets.
- Develop transformations and calculations needed for BI and reporting.
- Create or support analytical outputs using tools such as Power BI.
- Implement data quality checks validation rules and data governance best practices.
Operations & Continuous Improvement
- Troubleshoot pipeline/data issues identify root causes and provide long-term solutions.
- Optimize performance cost and reliability across all data workflows.
- Collaborate with business product analytics and IT teams to translate requirements into technical solutions.
- Maintain documentation data lineage and change management practices.
