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Requirements: Experience with Snowflake, data engineering methodologies, DataOps, Talend, AWS basics, Qualtrics basics. Requirements: Experience with Snowflake, data engineering methodologies, DataOps, Talend, AWS basics, Qualtrics basics Responsibilities: Building structured, high-quality data solutions for pharma clients.
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Data strategy, data engineering and data management fundamental advantage - strong Salesforce Data Cloud / Snowflake / Databricks hands-on exposure preferred. A broad range of experience in large-scale database and data warehousing technology, like Snowflake or Databricks, as well as ETL processes, analytics and cloud technologies, Data Engineering, Data Science.
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Technology : Azure ADF/ ADB, Snowflake + Any BI Tools ( Tableau / Qlik) + AI/Client , GenAI. - Ability to build Azure cloud data solutions and provide technical perspective and expertise on storage, platform services, cloud architecture and RDBMS/ODS database practices (Snowflake and Databricks.
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Tools: SQL Developer, Snowflake, SnowSQL, Postman, IntelliJ/Eclipse, Jenkins, Git repositories, Source-Tree, RestAssured and Docker. Experience required in software engineering, Java, Python, Power BI, OBEE Reporting, Snowflake, SQL, and Selenium.
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These technologies include but are not limited to Microsoft, SAP, Qlik, Snowflake, Alteryx, Automation Anywhere, and Workday Adaptive Insights. These technologies include but are not limited to Microsoft, SAP, Qlik, Snowflake, Alteryx, Automation Anywhere, and Workday Adaptive Insights.
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Nous maîtrisons l’essentiel des solutions techniques nécessaires : DBT, Talend, Fivetran, Snowflake, BigQuery , Power BI, Qlik, Tableau, Azure Devops, Google Cloud Build etc. Maîtrise du SQL et d’au moins un SGBD (PostgreSQL, Snowflake, BigQuery, etc.
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Primary responsibilities will include analysis in our many data sources (Enterprise Data Warehouse, Snowflake, Informatica, MSFT Dynamics, Big Data, Google BigQuery, Adobe Experience Platform) to build recommendation and propensity models as well as working with Data Engineering to identify, clean and integrate data into destination stores.
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The ideal candidate will possess deep technical expertise in Python programming, Snowflake data warehousing, AWS cloud services, Kubernetes (EKS), CI/CD methodologies, Apache Airflow, dbt, Kafka for real-time data streaming, and API development.
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The ideal candidate will have a strong background in utilizing Databricks for data exploration and analysis, alongside working knowledge of Snowflake and dbt. Work with Snowflake and dbt to manage data pipelines, ensure data integrity, and optimize data processing workflows.
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Familiarity with big data platforms, such as Azure Databricks, Azure HDInsight, or Azure Stream Analytics. Your proficiency in data modeling, database design, and data warehousing will serve as the bedrock for pioneering innovations, coupled with hands-on experience across various data sources and platforms, with a specific emphasis on Microsoft Azure.
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Experience in large scale cloud data migrations using Snowflake, Python, Spark, SQL, Databricks. Experience in end-to-end implementation of projects in Azure, Snowflake, Python, SQL. Technology|Analytics - Packages|Python - Big Data, Technology|Cloud Integration|Azure Data Factory (ADF), Technology|Data on Cloud-DataStore|Snowflake.
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You will be utilizing Snowflake and/or Databricks tools, cutting-edge DevOps technologies, developing advanced analytics products, and applying data and statistical programming tools to enterprise data to advance and enable key mission outcomes within cloud environments (GCP, AWS, Azure.
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Additionally, you will help us build resilient and scalable DevOps solutions, introduce containerization technologies, and manage our Elasticsearch, Snowflake, and dbt cloud instances. Manage and optimize our Elasticsearch, Snowflake, and dbt cloud instances to ensure high performance and availability.
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Ability to build AWS cloud/Snowflake data solutions and provide technical perspective and expertise on storage, platform services, cloud architecture and RDBMS/ODS database practices (Snowflake and Databricks.
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Production experience with: HDFS, YARN, Hive, Spark, Kafka, Oozie / Airflow, Amazon Web Services (AWS), Docker / Kubernetes, Snowflake. Database technologies (e.g. PostgreSQL, Redshift, Snowflake, and Greenplum.
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snowflake job
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