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Work you’ll do Work with a team with respect to discovery, design and delivery of data models, data governance models, data migration plans, data warehouse design and reporting components of applications using GCP, Databricks and Microsoft tools.
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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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Implement Azure products and services, including Azure Data Lake Storage, Azure Data Factory, Azure Functions, Event Hub, Azure Stream Analytics, Azure Databricks, etc., as well as conventional data warehouse tools, to create data migration and data engineering solutions.
$125,000 - $190,600 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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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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5+ years of experience with Azure data platform including Azure SQL, Azure Data Factory, Azure Databricks, Azure SQL Data Warehouse (Synapse Analytics), Azure Data Lake Storage, Azure Cosmos DB, SQL Server, SSIS, SSRS, etc.
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Client is hiring a Senior AWS Data Architect/Engineer with a strong expertise in HL7, to design and build our AWS Data Warehouse (data models, data architecture, data apps, data governance, data pipelines.
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Provide ETL design, development, implementation, and support of the customer's Business Intelligence & Business Analytic data for the warehouse. Familiarity with data warehouse design and implementation.
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Knowledge of DBT, Airflow, Ansible, Terraform, Argo, Helm, or other data pipeline systems; ideally experience building and maintaining a data warehouse and understanding of simple data science workflows and terminology.
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Responsible for the administration, configuration, and optimization of the Databricks platform to enable data analytics, machine learning, and data engineering activities within the organization.
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Proven experience as a Data Engineer, working with large-scale data pipelines, data warehouse architecture, and ETL processes. As a Data Engineer, you will play a critical role in developing and maintaining our data analytics infrastructure and data warehouse architecture.
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We are looking for a Cloud Data Engineer, who will be responsible to plan, design, develop and maintain the data architecture, data models, data pipelines and standards for various Data Integration & Data Lake and Data Warehouse projects in the AWS Cloud.
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Familiarity with cloud-based data platforms (e.g., AWS Redshift, Google BigQuery, Azure SQL Data Warehouse) and related services. ETL/ ELT: Experience in designing and implementing ETL / ELT processes to extract, transform, and load data from various sources into the data warehouse.
$190,000 - $200,000 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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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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Must have: Ab Initio Apache Spark AWS DevOps, CI/CD NoSQL OCP, Kubernetes PySpark Python Programming Snowflake Data Warehouse SQL Teradata Unix, PL/SQl, Oracle, Rest API, Java, Linux, Shell Scripting.
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Collaborate with the Finance or MP&L Application owners and the Ingestion team to “Land” and “Transform” the data in the Data warehouse on GCP BigQuery. Create data warehouse objects of raw data, and Analytical Model objects which are data products of customized data environments in GCP BigQuery (Data Warehouse), crafted to facilitate access of the information, with the goal of supporting scalable growth, sustainability, reusability, faster analytical solutioning and to accelerate delivery of business value.
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