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WHAT THIS ROLE WILL DO Solution Design: Develop comprehensive data warehouse and platform architecture blueprints, encompassing data modeling, ETL processes, data integration, storage, and data governance.
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Architect, develop, and document scalable data architecture patterns, ETL frameworks, and governance policies, adhering to Databricks’ best practices to support future and unknown use cases with minimal rework.
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Conduct data management maturity assessments and identify pain points for including data quality, governance, architecture, analytics, metadata management, master data management.
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Must have experience in architecting and implementing data architecture, data engineering, reporting and analytical solutions across multiple business functions and domains. Data Strategy and Governance.
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This role requires a strong technical background in data platform architecture, cloud computing, and big data technologies. Participate in platform solution architecture, including data architecture.
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End-to-end supply chain of data including, but not limited to, AI and Advanced Analytics, Business Intelligence, Cloud enablement, Data Privacy, Data Science, Digital Transformation, Data Governance / Data Management, Enterprise Data Architecture, RPA/Intelligent Automation, Risk and Regulatory Compliance.
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Advanced degrees, certifications and significant experience in data architecture, data management, Data Science, Azure Cloud Big Data technologies, Azure Data Lake Storage, Kafka, Air Flow, Stream Analytics, Databricks, Spark, Databricks SQL Analytics, Notebooks, MLflow, Snowflake, Snowflake SQL, Snowpark, Cosmos DB, Alation, Python, R, Java, Scala, Azure functions, Advanced Analytics and AI Client libraries, KsqlDB, FHIR/Rest API, GraphQL API, Power BI etc.
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Data Strategy/ Data Product Management Qualifications:Demonstrated expertise in Data Governance principles and practices (Data Stewardship, Data Security, Data Quality, Data Catalog, Data Use, Data Retention Policy, Master Data Management.
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May recommend data analytics strategies including enterprise data warehousing, master data management, data quality, data governance, Big Data, business intelligence, visualization.
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Develop and maintain data management and governance standards, policies, and best practices, including metadata management, data quality management, master data management, data lineage, and other related areas.
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Understand various data storage solutions, including extract, load and transform technologies built using our Databricks Lakehouse architecture on Microsoft Azure, working with the Data Engineering team as needed.
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Develop and oversee a comprehensive data architecture, aligning with business goals and integrating technologies such as Azure, Databricks, and Palantir to craft a forward-looking data management and analytics landscape.
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In architecture, design and development of data integration (ETL) solutions, including data quality and master data management - primarily using Microsoft technologies; + Master Data Management, Metadata Management, Data Governance, Data Strategies and Assessments, Data Modeling.
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Firsthand experience implementing data solutions leveraging Python, Spark SQL, Databricks Lakehouse Architecture, Kubernetes, Docker, preferred. Maintaining familiarity with the technology stack available in the industry for metadata management: Data Governance, Data Quality, MDM, Lineage, Data Catalog, etc.
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The Digital Analytics Manager will lead and develop a team of data architects and engineers in a transition that brings together our current data architecture and reporting with contemporary cloud technologies, enabling dynamic data storytelling through visualizations and empowering data scientists to mine for insights that help PNNL make better business decisions.
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