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5+ years in designing, solutioning the Snowflake, Databricks and Big data tools including DataOps, Data engineering, Data Integration, Data Orchestration and Data Marketplace.
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May recommend data analytics strategies including enterprise data warehousing, master data management, data quality, data governance, Big Data, business intelligence, visualization.
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Data Storage and Processing: Knowledge in managing data lakes, warehouses (e.g., Snowflake, Databricks, Redshift), and big data frameworks for batch and streaming architectures.
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Experience using Big Data or Cloud integration technologies such as Databricks, Matillion, Azure Data Factory, AWS Glue, AWS Lambda, etc. Expertise and hands-on experience developing and deploying SQL, Python, and Spark-based data pipelines into production environments.
Full-timeExpandApply NowActive JobUpdated 24 days ago - UpvoteDownvoteShare Job
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2-4 years of experience in modern data technology stacks such as Google Big Query, Snowflake, Databricks, etc. Facilitate seamless data integration and transformation to ensure effective data flow across various applications, keeping in sync with data mart and Adobe CDP environments.
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Big Data and Analytics: To handle and analyze enormous volumes of data, Data Engineer should have experience use big data technologies like Azure Databricks and Apache Spark to create data processing workflows and pipelines, to support data analytics, machine learning, and other data-driven applications.
Full-timeExpandApply NowActive JobUpdated 8 days ago - UpvoteDownvoteShare Job
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Design, develop and deliver data integration/data extraction solutions using IBM DataStage or other ETL tools and Data Warehouse platforms like Teradata, BigQuery. Job Role: GCP Data Engineer.
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2+ years working Extensive experience in Big Data Engineering 1+ years working experience in Python-based data integration and pipeline development. development 1+ years of Experience with AWS/GCP Cloud or equivalent on data integration with Glue, Kafka, Elastic Search, Lambda, S3, Redshift, RDS, Kubernetes, or BigQuery ecosystems.
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Analyzing next-generation sequencing (NGS), functional genomics, statistics for big data analysis, or multi-omics data integration. Design, implement and/or deploy NGS data analysis workflows for data processing, visualization, integration and mining to support Client therapeutic target identification and disease biomarker discovery.
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Implementing BIG-DATA platform for HIE interfaces using Mongo DB, Apache Spark, HDFS, Zookeeper, and Ambari. Develop Data Ingestion and Integration from data sources to Data Lake and/or Data Warehouse.
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You will have a good understanding of business intelligence tools, data warehouses, master data management, data integration, documents and content, data modelling and design, ML/Big Data, analytics and dashboarding.
Full-timeExpandApply NowActive JobUpdated 9 days ago - UpvoteDownvoteShare Job
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Work with big data technologies like Azure Databricks, Apache Spark & Palantir. Experience with Data integration, data processing, data streaming, message queuing techniques.
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The successful candidate will have experience with more than one of the following; analyzing next-generation sequencing (NGS), functional genomics, statistics for big data analysis, or multi-omics data integration.
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We are seeking a Data Engineer r or ETL/data integration/big data developer with experience in projects emphasizing data processing and storage. Big data concepts and technologies, such as: Azure Synapse & Databricks.
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NET Core , MVC, Entity framework, Unity, o JQuery, knockout, Angular, JS libraries, CSS Bootstraps, Visual Studio 2017, REST, LINQ, ArcGIS, ESRI, SQL Server, Dynatrace, o Cloud Managed SQL, Big data/data lakes.
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