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5 - 8 years of hands-on experience in MDM systems integration, data integration, data governance systems, with a proven track record of successful project implementations involving Reltio and Alation or Collibra.
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Hands on experience architecting and delivering solutions using the Azure Data Analytics platform and solutions including but not limited to Azure Databricks, Azure Cosmos DB, Azure Data Factory, Azure Logic Apps, Azure Functions, Azure Storage, Azure SQL Data Warehouse, Azure Data Lake, Azure Search, and Snowflake.
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Must have extensive hands on experience in designing, developing, and maintaining software solutions on Big Data platform such as Hadoop eco-system. Must have extensive hands on experience in designing, developing, and maintaining software solutions in Hadoop cluster.
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4+ years of hands-on experience developing a distributed data processing platform with Hadoop, Hive,Scala, Airflow or a workflow orchestration solution are required. Mentor junior engineers on the team Lead daily standups and design reviews Groom and prioritize backlog using JIRA Act as the point of contact for your assigned business domain Requirements: 8+ years of hands-on experience with developing data warehouse solutions and data products.
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Hands-on experience with SQL, R, and PythonExperience with Hadoop, Spark, and AWS computing preferred Responsibilities Marketing Science Internship You will learn how to work with production code bases and translate data into visualization models that will be used on actual client projects.
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5 plus years of hands-on experience with Python and/or R programming and statistical. environments (such as Splunk, Hadoop, Spark, Flink, Stream Analytics, Kafka, Docker, based on Cloud infrastructure and services such as Azure,GCP,AWS cloud platforms and Onpremise.
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Hands on experience with AWS /EMR, S3, Glue, Athena and Kubernetes Infrastructure. Expertise in designing, implementing and administering large Hadoop clusters and related Infrastructure such as Hive, Spark, HDFS, HBase, Oozie, Presto, Flume, Airflow and Zookeeper.
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As a Data Engineering Principal for a leading digital agency, you will be a hands on, senior data technologist. Hands-on experience of modern software delivery, including CI/CD and DevOps practices with a deep understanding and extensive track record of delivering complex data solutions using Agile methods including Scrum, SAFe etc.
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This is a hands-on analytical role for a proactive internal consultant to the access strategy, precision medicine (PM) and oncolody nurse education (ONE) insights and analytics team. 2+ years of recent experience and proficiency with Python, R, SQL and big data technology - Hadoop ecosystem (Cloudera distribution - Impala, Hive, Hbase, Spark, MapReduce etc.
ExpandApply NowActive JobUpdated 14 days ago - UpvoteDownvoteShare Job
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Hands-on development experience using open source big data components such as Hadoop, Hive, Pig, Spark, HBase, Hawk, Oozie, Mahout, Flume, Kafka, ZooKeeper, Sqoop etc. 8-10 years of software development and deployment experience with at least 5 years of hands-on experience with Hadoop applications (e.g. administration, configuration management, monitoring, debugging, and performance tuning.
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Hands-on experience with Google cloud Technologies like Bigquery, Cloud Storage and Cloud native ETL processing tools. Extensively worked on Teradat/Oralce/Hadoop as database using Ab Initio as ETL tool for large scale data integration.
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This would include hands-on with firewalls, IDS/IPS, and SIEM. Experience in forensics or general security consulting and familiarity with Managed Security Services a plus. · Experience with ArcSight, Splunk, IBM QRadar, McAfee Nirtro, and/or Hadoop, a must.
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Advanced hands-on experience implementing data migration and data processing using Azure services: Azure Data Factory, Azure Functions, Synapse, Azure SQL DB, Event Hub, Service Bus, Azure Stream Analytics, Azure Analysis Service, Azure Data Catalog, Cosmo Db. Intermediate experience with other BI tools such as Power BI, SSRS, SSAS and Tableau.
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Hands-on experience with cloud-based data platforms (e.g., AWS, Azure, GCP) preferably GCP and data Lakehouse architectures. Proficiency in data engineering tools and technologies such as SQL, Python, Spark, Hadoop, Airflow, Apache Kafka and Presto.
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Nice to have: Experience with AWS; Kubernetes / Docker; Jenkins; Hadoop/Spark; Kibana; Kafka; NiFi; ElasticSearch; NoSQL / MongoDB / document-oriented DB; AI/ML understanding Bachelors degree Security Clearance: Active TS/SCI with Polygraph Compensation for roles at Accenture Federal Services varies depending on a wide array of factors including but not limited to the specific office location, role, skill set and level of experience.
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