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Architect and drive the end-end Big Data deployment automation from vision to delivering the automation of Big Data foundational modules (Cloudera CDP), prerequisite components and Applications leveraging Ansible, Puppet, Terraform, Jenkins, Docker, Kubernetes to deliver end-end deployment automation across all ServiceNow environments.
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Minimum of 10 years of experience in big data, database and data warehouse architecture and delivery. Certified AWS Big Data Specialty (Nice to have) Extensive hands-on experience implementing data migration and data processing using AWS services: VPC/SG, EC2, S3, AutoScaling, CloudFormation, LakeFormation, DMS, Kinesis, Kafka, Nifi, CDC processing Redshift, Snowflake, RDS, Aurora, Neptune, DynamoDB, Cloudtrail, CloudWatch, Docker, Lambda, Spark, Glue, Sage Maker, AI/Client, API GW, etc.
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Experience or a high level understanding of Big Data technologies on a Hadoop platform (e.g. Informatica, Scoop, Hive, Pig, NoSQL) SKILLS: Senior Data Quality Assurance Engineer, Informatica, Tableau, QlikView, Quality Center, Data Strategies, Data Delivery, Hadoop, Data Quality Testing, ETL Testing, ETL tools, Oracle, SQL Server, SOAP UI, Jenkins, AntHill Pro, Bamboo, Quality Center, Agile Central (Rally), Scoop, Hive, Pig, NoSQL.
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Hands-on experience with “big data” technologies (e.g. Apache Spark, Google Dataproc, Google Big Query) and messaging technologies (e.g. Kafka, Message Queue (MQ), Java Message Service (JMS.
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Experience using big data technologies (Snowflake, Airflow, Kubernetes, Docker, Helm, Spark, pySpark) Stay on top of new technologies through R&D and prototyping to continuously improve our big data architectures and systems to streamline how we deliver value with high quality to our end users.
ExpandApply NowActive JobUpdated 17 days ago - UpvoteDownvoteShare Job
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Delivery experience in Data Engineering with Google Cloud Platform including Big Query, Dataflow, Cloud Data Fusion, Cloud Composer, Dataproc, Cloud Pubsub, Cloud Function etc. + Propose, own initiatives and demonstrate though leadership that improve the efficiency and productivity of the data science Center of Excellence.
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The Clinical Data Science team at Eisai, Inc., is expanding its capabilities by acquiring in-house talent related to bioinformatics, machine learning, and big data analytics for the Oncology and Neurology clinical programs.
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Perform integrative analyses of multi-omics data from clinical trials and research cohorts, including genomics, transcriptomics, proteomics, and imaging, to identify biomarkers related to treatment mechanism and response, and potential therapeutic targets.
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Strong expertise in Big data and data warehouse, with strong skills in HDFS/Hadoop/Spark using Java/Scala/Python, SQL, NoSQL, ETL, BI Reporting, Analytics. Data Architect with 15+ years of experience, specializing in Azure Databricks, Azure Data Factory (ADF), Cluster configurations, Azure Data services, Azure DevOps, Azure SQL, SSIS, Azure DW, Azure Analysis Services (Cube), Data Lake Storage, Blob Storage, Cosmos DB.
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Minimum of 3 end-to-end SAP Master Data Governance implementations. + Minimum 7 years of experience with/in SAP Master Data Governance. Accentures SAP practice, and we bring the New to life using design thinking, agile development methodologies, and the latest smart tech like IoT, automation, and AI. We even built myConcerto, a mix of SAP and Accenture that helps lead our clients to big wins.
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Experience as Data Engineer in Azure Big Data Environment. Demonstrated analytical and problem-solving skills, particularly those that apply to a big data environment.
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Experience using data visualization tools such as Tableau and R Shiny to create user friendly visualizations required. Use data visualization tools including Tableau and/or R-Shiny to design, develop, and maintain user-friendly data visualizations and dashboards.
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The ideal candidate will have expertise in cloud technologies, particularly Google BigQuery, DBT, and Looker along with working experience in onPrem systems such as Teradata, SAP HANA. You will be responsible for crafting, building, and maintaining our data infrastructure and analytics solutions.
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The Scientific Knowledge Engineering team, which sits within the Onyx Product Management organization, is responsible for the data modeling, ontology definition and management, vocabulary mapping, and other key metadata activities that ensure Onyx platforms and data assets speak scientific language.
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We are looking for an MDGO Data Governance Technical Data Steward with a track record of supporting enterprise data management tools and integration projects. To be successful in this role, the candidate would require a balance of product-centric technical expertise, and a deep knowledge of DG practices, which includes but not limited to metadata management, metamodeling, lineage documentation, data stewardship, and technical DQ processes.
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big data jobs Company: Dell in Raleigh, NC
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