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Must be proficient with Microsoft Dynamics 365, Data-verse, Microsoft Power-Platform, Microsoft Azure Technologies (Logic Apps, Data Lake, Storage, Data Factory), SQL (SSIS, SSAS, SSMS, SSRS), Power BI, Function Apps, Data Structure Design & Development, and Python.
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By combining tactical skills with a deep understanding of global Data Privacy Regulation (i.e. GDPR, CCPA), local data-centric regulations (i.e. SOX, GLBA), and industry best practices (i.e. AI Ethics, PII, IAPP and PCI credentials and certifications), Data Governance is a critical partner to Enterprise Risk, Information Security, Compliance, Privacy, and Legal to enable and drive business goals and FIS data strategy, while helping to protect from risk.
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Experience working with cloud-based data engineering and storage technologies such as AWS and orchestration tools such as Apache Airflow Astronomer. As a Senior Data Engineer, you will be responsible for the design, orchestration, monitoring, quality, accuracy, and security of data throughout its lifecycle, including data ingestion, collection, storage, processing, and analysis primarily using Snowflake, Fivetran and Astronomer.
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Leverage a modern tool stack including Snowflake, Atlan, Fivetran, Docker, AWS, and Astronomer (Airflow) to cultivate an environment where analysts and data engineers can autonomously enact changes in an automated, thoroughly tested and high-quality manner.
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Experience developing reporting and advancing data visualization capabilities with new or existing tools (e.g. Tableau, Looker, PowerBI, DataStudio, Qlik, TIBCO/Spotfire, etc.) Lead and directly support team(s) deployed to organizations to help understand and evolve their current data strategy, omni-channel analytics capabilities, and technology needs.
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In depth knowledge of modern data technologies including Snowflake, RedShift, Azure SQL/Synaspe, Databricks or similar technologies. Extensive experience or knowledge of data visualization technologies such as Tableau, Power BI, Qlik and Spotfire.
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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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Role : Data Engineer - Datastage ,DBT ,Snowflake. Data Engineer - Datastage , DBT, Snowflake. This role is specifically within Capital markets (Foreign Exchange, Commodities, Interest Rate Derivatives, Securities processing) and should be Principal/Lead level to drive productivity in the data space.
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Provide support for development of SAP SAC, Planning, Cognos BI applications, Power BI and data warehouse. Create and maintain documentation for existing architecture, data dictionaries, future data warehouse architecture and business intelligence reporting.
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We are seeking a skilled Data Quality Engineer to join our team and play a critical role in ensuring the reliability, accuracy, and consistency of data within our warehouse and manufacturing data platform in the Client supply chain space.
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Salary: Data Entry Work from Home - Part Time Computer Job. Computer Data Entry Work from Home Part Time Job. Work from Home Administration - Computer Online Data Entry & Short Studies/Feedback - Part Time & Full Time Work - No Experience Needed, Training Provided.
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Warehouse Cherry Picker Operator Responsibilities. Warehouse Cherry Picker Operator Requirements. Warehouse Cherry Picker Operator Benefits. Warehouse Cherry Picker Operator Responsibilities.
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Related experience in Snowflake administrationDatabase administration and data engineeringExperience using AWS servicesExtensive hands-on experience with Python,SQL and DBTSnowflake Pro certificationDeep knowledge of all Snowflake features, with a particular emphasis on advanced security measures (Data Masking, TriSecret Secure), data sharing, and application development.
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They have a backlog of DATA CENTER and LARGE INDUSTRIAL WAREHOUSE projects across the nation and need a Traveling superintendent ready to go where needed to complete these highly esteemed projects.
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Constantly look for useful attributes to expand our core data assets from various data sources, like US census bureau, and validate the usefulness of the new data. Utilize proper tools to clean customer data, like PySpark, R, Python, or Excel, to prepare for upcoming data analysis or modeling exercises.
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