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Integration and distribution of data through a single platform, with that platform being a high-performance, highly scalable, elastic and highly secure commercial cloud will enable labor, infrastructure and non-cap tech savings across all participating CDO's as well as in the Optum Care national level.
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Exposure to Amazon AWS or another cloud provider- Business Intelligence tools such as Tableau, ThoughtSpot, PowerBI and/or Looker- Familiarity with data warehousing platforms and data pipeline tools such as Redshift, Snowflake, SQL Server, etc.
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He/she should have prior knowledge of data integration, data warehousing, modeling, business intelligence, and data visualization / presentation concepts. Data Extraction / Data Integration.
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Experience with data modeling, data warehousing, data engineering, data integration. Understanding of relational databases (e.g., Snowflake, Google Big Query, Databricks, Redshift, Synapse), data warehousing, ETL, web services, SaaS technologies, etc.
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Comprehensive understanding of Microsoft Azure Data Platform, covering Azure Synapse, Azure Data Factory, Azure SQL Database, and legacy Microsoft SQL Server technologies such as Database Engine, SSIS, SSRS, and SSAS.
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Experience with designing and developing end to end data pipelines on AWS - Firm understanding of data modeling, master data management, enterprise data warehousing, ETL, reporting, querying, requirements analysis, and data integration techniques.
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Experience with data warehousing, ETL/ELT processes, and data integration platforms (i.e., Airflow, MuleSoft, SnapLogic). Define and implement a technical roadmap sought at driving the long-term scale of our BI and Data Warehousing platforms.
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3+ years of experience in ETL/ data integration is required with 2+ years of experience in Dell Boomi or similar technology. 5+ years of experience in data engineering, data warehousing, business intelligence, ETL on databases such as Oracle or SQL Server, Snowflake, and/or big data is required.
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Learn and demonstrate mastery across a wide variety of data engineering activities, including data warehousing, master data management, data cataloging, system integration, data streaming, data visualization, data analysis, and data ops.
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Solid understanding of data engineering concepts and techniques, such as data modeling, data warehousing, data integration, data pipeline, data lake, data quality, and data security.
ExpandApply NowActive JobUpdated 9 days ago - UpvoteDownvoteShare Job
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5+ years of experience in data engineering, integration, data warehousing, business intelligence, business analytics, or comparable role with relevant technologies and tools (Spark, Informatica/IICS or equivalent) – strong expertise in IICS / Informatica mandatory.
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Strong technical understanding of data modeling, data mining, master data management, data integration, data architecture, data virtualization, data warehousing, and data quality techniques.
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Knowledge and integration experience handling 2 ERP systems (SAP-ECC, SAP S4), Business Intelligence, Data platform, Third party tools like ShipERP, Trade compliance tools, etc. In your role as SAP S4 HANA IT Architect, Supply Chain you will play a technical leadership role within the SAP organization in Materials Management (MM), MRP, Logistics Execution (LE), Warehousing (EWM), Purchasing and Trade Compliance.
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Strong proficiency with data warehousing tools (Snowflake, SQL Server, Google BigQuery). In this position, the AI/ML Big Data Architect will play a critical role in assisting researchers by designing and implementing robust data architectures, optimizing AI/ML data workflows on large-scale systems, implementing best practices for data integrity and scalability, building and maintaining the distributed AI/ML data infrastructure, and ensuring seamless integration of cutting-edge data storage and retrieval techniques with research initiatives.
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Experience with data warehousing and ETL tools like Apache Spark, Apache Hadoop, or AWS Glue. Familiarity with cloud computing platforms like AWS, Azure, or Google Cloud. Key OutcomesHolistic Data Integration: Efficiently extract and integrate varied data sources, from mainstream platforms like NetSuite and Salesforce to unique data from our high-throughput labs and manufacturing sensors.
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