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Performs analyses, development, and evaluation of data mining in the Snowflake cloud data warehouse environment which includes data design, database architecture, and data management utilizing Informatica PowerCenter, Intelligent Data Quality, and Enterprise Data Catalog.
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Experience selling one or more of the following: Analytics, Back Office Solutions, Data Governance, Data Management, Data Warehouse, EOD Reporting, Front Office Solutions, Managed Services, Middle Office Solutions, Order Management (OMS), Portfolio Management (PMS), Reconciliation, Regulatory Solution, Reporting, Trade Management, Treasury.
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Etc into pyspark Strong knowledge of cloud native data platform architectures, data engineering and data management Good knowledge of popular database and data warehouse technologies from Snowflake Microsoft Azure SQL Data Warehouse Demonstrated knowledge of data warehouse concepts.
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Makes cycle count adjustments through variance data input into the Sysco Warehouse Management System (SWMS) to provide accurate information for planning and evaluation activities.
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Experience with modern data warehouse concepts and tools (Snowflake, Azure Data Lake, Databricks) The Senior DataOps Engineer is well versed in data management, integration, architecture, and technology principles and has a proven track record of partnering with Architecture, Data Engineering and Operations teams to shape data operations patterns that support Kiewit’s Data Strategy while also meeting stated operational availability, risk, and cost mitigation expectations.
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Advanced experience in SQL in big data warehouse systems such as Snowflake, BigQuery, Databricks, etc. As a member of the Data Engineering - Business ETL team, you own the data warehouse and data pipelines that are used for financial and regulatory reporting.
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Iron Mountain helps lower cost and risk, comply with regulations, recover from disaster, and enable digital and sustainable solutions, whether in information management, digital transformation, secure storage and destruction, data center operations, cloud services, or art storage and logistics.
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This position performs the task of developing a deep understanding of Oracle Fusion Analytics Warehouse (FAW), Oracle Data Integrator (ODI) marketplace, Autonomous Data Warehouse (ADW), Oracle Analytics Cloud (OAC), and Oracle Cloud Infrastructure (OCI) to support the Center of Excellence in providing tailor-made and Oracle-delivered Reports to users.
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Continually improve the operations, monitoring and performance of the data warehouse. Experience with workflow orchestration management engines such as Airflow, Dagster, DBT, etc.
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4+ years of professional data engineering experience. Being a domain expert on data modeling, data pipelines, data quality and data warehousing of financial data.
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Experience in building scalable infrastructure to support batch, micro-batch or stream data processing for large volumes of data. Designing, building and maintaining data ETL/ELT pipelines to source and aggregate the required data for various data analyses and reporting needs.
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Internal knowledge of open source or related big data technologies. Collaborating on the design, deployment and continuous improvement of a scalable data platform that ingests, stores, and aggregates various datasets.
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Inventory, Forklift, Lifting boxes, Inventory control, Shipping & receiving, Pallet jack, Material handling, Data entry, Inventory management, Shipping and receiving, Bill of lading, Fedex, Warehouse inventory, Loading, MIG, bond, fuse, horizontal, melt, metal, stainless, steel, tig, vertical, weld*, welding, Mig welder, Press brake, Fitting, Cycle counts, Sap, Picking.
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Experience with Proliance, Data-Warehouse, or similar systems preferred. Present data to management in an organized manner. Knowledge of cost/schedule management, earned value management, change management, forecasting, and risk assessment.
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Circle is a financial technology company at the epicenter of the emerging internet of money, where value can finally travel like other digital data — globally, nearly instantly and less expensively than legacy settlement systems.
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