data pipeline. · Data Validation and Quality Assurance: Data Validation and Quality Assurance: -Develop Databricks environment. -Perform data profiling, data quality checks, and data reconciliation to identify with data engineers and analysts to establish data quality standards and implement data validation rules Familiarity with data testing frameworks and tools for data quality assurance and validation. 3. Experience in developing solutions. 9. Knowledge of data governance, data quality management, and data compliance regulations
business metrics and trends. Design and implement data quality and data validation processes to ensure the business metrics and trends. Design and implement data quality and data validation processes to ensure the
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Data Quality Management:
Develop and implement a data quality framework with
of data within Global Markets.
Establish data quality standards, metrics, and monitoring mechanisms
mechanisms to continuously assess and improve data quality.
Provide guidance and support to data owners
owners and stakeholders on data quality management best practices and tools specific to Global Markets.
Detail-oriented with a strong focus on accuracy and data quality. Ability to work independently and manage multiple
Detail-oriented with a strong focus on accuracy and data quality. Ability to work independently and manage multiple
data platforms (AWS, GCP, Azure). Data architecture, governance, quality, security regulations and API integrations
high-quality solutions for data sources, reports and dashboards that meet quality criteria according to BI
high-quality solutions for data sources, reports and dashboards that meet quality criteria according to BI
business requirements and best practices. Ensure data quality, consistency, and security across Salesforce
business requirements and best practices. Ensure data quality, consistency, and security across Salesforce