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Serves as a primary contributor for the Enterprise Data Catalog, Master Data Management Metadata repositories, and manages the health and well-being of the repository using the tool Informatica Axon EDGC Business Process Management workflows.
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Should have in depth understanding on Data Warehousing, Master Data Management (MDM), Data Quality, Data Lineage, Data Modeling, Data Profiling and Data Policy and handled Data Governance programs.
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Assists the Aquaculture and Marine Resource Specialists with a variety of shellfish, finfish, aquaculture and water quality research and monitoring projects Data collection and entry into databases as well as data analysis for incorporation into scientific publications and presentations.
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Will assist in the rollout of CNB’s Data Governance reporting and strategy structure that includes: Data Governance and Policy, Data Quality and Control, Data Strategy and Architecture, Master Data Management, and Analytics Data Management.
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Expertise in data engineering, data quality, data validation, ETL tools, Master Data Management, Metadata, data privacy and security. Enhance Quartzs enterprise-wide data and reporting capabilities Optimize technology in the data space to create a competitive advantage within the healthcare and health insurance industries Promote data literacy and a companywide data-driven culture Establish policies and practices that improve user access to data while ensuring data quality, credibility, protection, and privacy through effective data governance.
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Engage with the Data Governance, Data Stewardship, Data Privacy, Master / Reference Data Management, Data Architecture, BI and Data Warehousing, Data Science, and other teams to ensure data quality and metadata management activity aligns with the other capability needs including the Global Data Governance Accountability Framework.
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Experience with data architecture principles, practices, tools, and concepts such as data quality, master data management, data lineage, metadata management, and data governance.
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What you’ll be doing:As a senior business solution architect (BSA) for master data management (MDM) & data quality management (DQM) focused on end-to-end engineering & manufacturing (Engg. & Mfg.) and product lifecycle management (PLM) processes, you will play a critical role in enabling data architecture and governance to help drive standardized and sustainable enterprise data footprint.
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Understanding of modern data architecture and experience with core data services like maintaining data lakes, data warehouses, data marts and relational and dimensional modeling, data quality and master data management.
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Experience with master data management, data quality and/or data governance. Collaborate with supply chain, quality control, quality assurance, IT, finance, and sales organizations to determine business requirements for the design of master data.
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Support the execution of data quality management, master data management (MDM), and metadata administration and management process/processesAssist with the update, maintenance, and completeness of data records, in the data inventory, data registry, and data catalog, and provide status reports and metrics on items such as accuracy, completeness, due dates for actions, and discrepancies.
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Designing, developing, testing and implementing solutions for the data management functions including metadata, master data and data quality management, with SQL, C#, Power BI, Azure Data Factory, Logic Apps, Event Hub and other vendor platforms.
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As a Principal AI/ML Engineer ,you will be responsible forbuilding machine learning based solutions for Data Integration, Data Quality, Master Data Management, Data Governance, and other Information Management solutions.
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Knowledge of Master Data Management (MDM) and Data Quality tools and processes. Experience with Visual Studio, PowerShell Scripting, ARM templates, Python, JavaScript, Data Modeling, Version Control, Data Transformation, Data Quality Testing, Data Documentation & Lineage, Data Orchestration, CI/CD, Data Visualization, SSIS, SSRS, and Git/Azure DevOps.
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Data Engineering: Extensive knowledge of data engineering, data integration, and data management concepts, such as APIs, ETL (Extract, Transform, Load), MDM (Master Data Management), CRUD (Create, Read, Update, Delete), Pub-Sub (Publish-Subscribe.
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