Fa Etqo Saasfaprod1.fa.ocs
Big Data Lead
United States
Sponsorship not specifiedDetected 17 days ago
PythonData Engineering
About the role
- Responsibilities: • Development and Maintain Data Pipelines: Design, implement, and optimize end-to-end ETL/ELT pipelines for ingesting, processing, and transforming large volumes of structured and unstructured data. • Utilize Python and Pyspark: Write efficient, scalable and maintainable code in Python and leverage Pyspark for large-scale data processing
- in distributed computing environments. Also be able to review existing code and identify areas of improvement. • Ensure Data Quality and Integrity: Implement data validation, cleansing, transformation and reconciliation processes to ensure data accuracy and consistency throughout the data lifecycle. • Collaborate with Stakeholders: Work closely with IT
Responsibilities
- Development and Maintain Data Pipelines: Design, implement, and optimize end-to-end ETL/ELT pipelines for ingesting, processing, and transforming large volumes of structured and unstructured data.
- Ensure Data Quality and Integrity: Implement data validation, cleansing, transformation and reconciliation processes to ensure data accuracy and consistency throughout the data lifecycle.
- Collaborate with Stakeholders: Work closely with IT teams and business stakeholders to gather data requirements and translate them to technical solutions.
- Troubleshoot and Optimize: Monitor job performance, troubleshoot complex data issues and fine-tune for performance and scalability.
- Development and Maintain Data Pipelines: Design, implement, and optimize end-to-end ETL/ELT pipelines for ingesting, processing, and transforming large volumes of structured and unstructured data. • Utilize Python and Pyspark: Write efficient, scalable and maintainable code in Python and leverage Pyspark for large-scale data processing in distributed computing environments.
- Design, implement, and optimize end-to-end ETL/ELT pipelines for ingesting, processing, and transforming large volumes of structured and unstructured data.
- Implement data validation, cleansing, transformation and reconciliation processes to ensure data accuracy and consistency throughout the data lifecycle.
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