Data Pipeline Management: Design, develop, and maintain scalable data pipelines to support business intelligence and reporting needs.
ETL Implementation: Develop ETL processes to extract, transform, and load data from various sources into operational data stores while ensuring data integrity and consistency.
Data Modeling: Design and implement data models, schemas, and dimensional models to support analytical reporting and ad-hoc queries.
Data Governance: Implement and enforce data governance policies and procedures within the data operations environment to ensure data quality, compliance, and security standards are upheld.
Documentation and Collaboration: Document data operations processes, data flows, and system architectures. Collaborate with cross-functional teams to drive data-driven decision-making and enhance data accessibility and usability.
Operational Monitoring: Monitor data pipelines and workflows to ensure optimal performance, identifying and resolving issues promptly to maintain operational efficiency and reliability.
Skills Required
Machine Learning
Python
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