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Familiarity with the technology stack available in the industry for metadata management: Data Governance, Data Quality, MDM, Lineage, Data Catalog etc. Minimum of 10 years of experience in big data, database and data warehouse architecture and delivery.
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Strong Knowledge of Data Warehouses/Lakehouse architectures, Master Data Management, Data Quality Management, Data Integration, and Data Warehouse architecture.
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AWS services, big data, data warehouse architecture, Extensive hands-on experience implementing data migration and data processing using AWS services: VPC/SG, EC2, S3, Autoscaling, CloudFormation, LakeFormation, DMS, Kinesis, Kafka, Nifi, CDC processing Redshift, Snowflake, RDS, Aurora, Neptune, DynamoDB, Cloudtrail, CloudWatch, Docker, Lambda, Spark, Glue, Sage Maker, AI/Client, API GW, etc.
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That users can determine what data is available in the warehouse, how this data is defined, and lineage to Epic. to design, create and publish data models, ETL processes, and metadata using the Epic Caboodle Console.
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SAP Master Data Business Analyst's primarily responsible for implementing and managing relevant Master Data of SAP S/4HANA covering the Make, Quality, and Warehouse Management areas.
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Design, develop and manage Lenox Cloud based (AZURE) in-house data warehouse, reporting and dashboard platforms, integrations, and interfaces between D365, legacy Lenox data and 3rd party vendor software systems.
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Data Modeling: Requires knowledge of cloud data strategy, data warehouse, data lake, and enterprise big data platforms; Data modeling techniques and tools (For example, Dimensional design and scalability), Entity Relationship diagrams, Erwin, etc; Query languages SQL / NoSQL; Data flows through the different systems; Tools supporting automated data loads; Artificial Intelligent - enabled metadata management tools and techniques.
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Demonstrable experience in Relational, NoSql, Distributed databases, Data warehouse, Data Lake and Streaming technologies, Master Data Management, reporting and analytics. Enterprise scale expertise in data management standard methodologies such as data integration, data security, data warehousing, data analytics, metadata management and data quality.
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Proficient in Epic event logging data model (metadata) and certified in EPIC EMR Clarity data warehouse reporting, Cogito Reporting workbench, and Cogito Radar applications, with advanced expertise in EPIC data and EPIC Clarity Data Model.
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Deep expertise in design and implementation of enterprise data store, data warehouse, data governance, data quality management, real-time data ingestion, data modeling, and metadata management, in tools such as Azure Synapses or Snowflake.
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Minimum 5 years of designing and implementing an operational production grade large-scale data solution on Microsoft Azure Snowflake Data Warehouse. Strong understanding or Snowflake on Azure Architecture, design, implementation and operationalization of large-scale data and analytics solutions on Snowflake Cloud Data Warehouse.
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Able to develop EL pipelines in and out of data warehouse using combination of Databricks, Python and Snow SQL. Hands-on experience with Snowflake utilities such as Snow SQL, Snow Pipe, Python, Tasks, Streams, Time travel, Optimizer, Metadata Manager, data sharing, and stored procedures.
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Create and maintain critical data documentation and metadata that allows data to be understood and leveraged as a shared asset. Data Strategy: Requires knowledge of understanding of business value and relevance of data and data enabled insights / decisions; Appropriate application and understanding of data ecosystem including Data Management, Data Quality Standards and Data Governance, Accessibility, Storage and Scalability etc; Understanding of the methods and applications that unlock the monetary value of data assets.
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SSIS strongly preferredPython DBT, Mulesoft, Alation – good to havePrimary Responsibilities:Strong understanding or Snowflake on Azure Architecture, design, implementation and operationalization of large-scale data and analytics solutions on Snowflake Cloud Data Warehouse.
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Good experience in end-to-end implementation of data warehouse and data martsExperience and detailed knowledge with ETL, Data Quality, metadata management, Data Engineering, streaming data loadsExperience designing and implementing complex solutions for distributed systemsGood to have experience in SnowflakeExperience in leading and mentoring teams in onshore-offshore model.
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