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Experience working with large datasets and big data technologies, preferably cloud-based, such as Snowflake, Databricks, or similar. Technologies generally leveraged to fulfill the work include, but not limited to, SQL, Python, Docker, Gitlab, Airflow, Lambda, Snowflake, and PostgreSQL.
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You will be responsible for utilizing your expertise in Microsoft Dynamics AX/D365 ERP, Microsoft Excel, SQL, and Accounting Information Systems to extract, transform, and analyze data. Utilize Microsoft Dynamics AX/D365 ERP to extract and manipulate financial data for analysis.
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Experience with one or more of the following data analysis tools such as: Alteryx, Qlik, Google Data Studio, R Studio, Tableau, Oracle, Microsoft SQL Server, Visual Basic, Microsoft.
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Previous experience with data warehousing, reporting overlay, database management software such as Snowflake, Alteryx, Microsoft Power Suite, SQL, or similar tools. This position serves as the subject matter expert (SME) on systems, reporting, data and tools used by the Treasury Operations & Accounts Payable organizations including: Kyriba Treasury Management System (TMS), bank portals, data analytics tools which may include Alteryx and/or Microsoft Power Suite.
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Expertise in Snowflake – data modeling, ELT/ETL using Snowflake SQL, Stored Procs, Snowflake Administration and knowledge in Big Data areas like Hadoop and Talend (optional.
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Our capabilities range from C5ISR, AI and Big Data, cyber operations and synthetic training environments to fleet sustainment, environmental remediation and the largest family of unmanned underwater vehicles in every class.
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Assist in the preparation of various plans including hazard mitigation and community redevelopment plans including research and analysis of socio-economic, demographic, environmental, historical hazard data, etc.
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Come make a big splash with us at Aquatica Park. Join us at Aquatica for one of our upcoming Open Call Lifeguard Swim Tests on Thursday or Saturday! Come make a big splash with us at Aquatica Park.
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Experience with both SQL and Oracle Data Warehouses. - Proficiency in code management, Analysis Services, Power BI, SSRS, Microsoft tools; Azure DevOps is most definitely a big plus.
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Google Dataflow/Beam or Spark, SQL, BigQuery or similar data warehouse. We use Scala, in combination with large-scale data processing and open-source technologies, to build our device graph.
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Data Protection Platforms: Dell EMC, Rubrik, Veeam, Cohesity, Commvault. Highly skilled teams of engineers and solutions architects with deep expertise across cloud, security, networking and modern data center infrastructure help customers acquire, deploy and operate technology that delivers impactful business outcomes.
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Experience with processing large datasets and building code using SQL, Python, pySpark & Apache Airflow / Amazon MWAA.Experience of near Real Time & Batch Data Pipeline development. 9+ years of applied experience in Data Engineering, including but not limited to building Data Pipelines, Orchestration, Data Modeling & Data Lake. Programming skills in one or more of the following: Python, Java, Scala, SQL, and experience in writing reusable/efficient code to automate analysis and data processes.
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Job DescriptionPOSITION PURPOSEResponsible for the development, implementation, and support of AVEVA (Wonderware) System platform/SQL Server-based factory management and control systems (FMS) within CHEP’s plants.
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Review and refine the information security strategy for HOS, in line with the CISO objectives, including the management of the HOS security organization and optimization opportunities with the Central CISO Teams (Compliance, Security Operations GSOC, Awareness campaigns, Data Privacy), and plan and manage HOS CISO security budget.
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The Data Scientist is expected to pull data from source systems; explore, validate, clean, and transform the data; leverage a wide range of algorithms and analytical approaches including Business Intelligence (BI), Machine Learning (ML), Natural Language Processing (NLP) and Artificial Intelligence (AI); deliver intelligence with full reproducibility; test and deploy the intelligence products following MLOps and DataOps best practices; and communicate results and insights to key stakeholders.
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