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The Data Quality Lead Analyst contributes to efforts to ensure data that is sourced and provisioned meet all required data quality standards. The Data Quality Lead Analyst leads the continuous Data Quality by assessing, evaluating and analyzing data, setting controls and guidelines for measurement, evaluation, adoption and communication of Data Quality and Data Quality risk. They collaborate with others to monitor and remediate data concerns.
Job Responsibility:
Contributes to efforts to ensure data that is sourced and provisioned meet all required data quality standards
Leads the continuous Data Quality by assessing, evaluating and analyzing data, setting controls and guidelines for measurement, evaluation, adoption and communication of Data Quality and Data Quality risk
Collaborates with others to monitor and remediate data concerns
Requirements:
Experience with Ab Initio Metadata Hub for metadata management, schema customization, and metadata extraction
Familiarity with Application Governance principles and integration with metadata management tools
Proficiency in using Control>Center for job scheduling, monitoring, and automation
Hands-on experience with Semantic Discovery for data profiling, analysis, and metadata enrichment
Ability to review, approve, and manage metadata proposals using the Metadata Portal
Strong understanding of data governance, data modeling, and metadata-driven workflows
Understanding of metadata management concepts and data modeling
Familiarity with Metadata Hub schemas (object model, metamodel, view model)
Ability to customize and extend Metadata Hub schemas to meet organizational requirements
Experience with the Metadata Portal for viewing, reporting, and analyzing metadata
Skills in extracting metadata from various sources (Mainframe, SAS, Ab Initio , Oracle, Teradata, Hive, SQL Server, Erwin, Cognos , Tableau ..etc.) and loading it into Metadata Hub
Ability to write Ab initio extractor graphs using webservice, Transform, Translate components for metadata ingestion
Experience monitoring and managing jobs using Control>Center
Understanding of job schemas, job definitions, and job metrics
Ability to automate and schedule analysis jobs and interpret job status and metrics
Familiarity with integrating Semantic Discovery with Control>Center for automated workflows
Knowledge of profiling and analyzing datasets to propose metadata linkages