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Experience leveraging complex data to drive business decisions, hands on experience in data science methodologies (predictive analytics, machine learning, patient level data triggers) using R, Pytong, Databricks and deep knowledge of Qlik, PowerBI, Tableau for visualization.
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Contribute to the development and education plans on data engineering capabilities, systems, standards, and processes. Strong understanding and hands-on experience with cloud platforms such as AWS, Azure, or Google Cloud Platform (GCP.
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Hands-on experience with big data technologies such as Apache Spark, Hadoop, and/or Databricks. 8+ years of experience in data engineering, with a focus on designing and implementing data architectures and pipelines.
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The ideal candidate will have a strong background in data engineering, with expertise in Databricks, DevOps tools (Jenkins/Terraform), and data modeling concepts (3NF, Dimensional, Data Vault.
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Sr. Azure Data Engineer with hands-on experience with Azure Data Analytics services e.g. ADLS, Azure Data Factory, Azure Databricks, Purview, Azure Synapse, etc. Hands-on experience with Azure Data Analytics services e.g. ADLS, Azure Data Factory, Azure Databricks, Purview, Azure Synapse, etc.
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Data Engineering: Design, build, and maintain scalable data pipelines and ETL processes using Databricks and other relevant technologies. Hands-on experience with DevOps tools such as Jenkins and Terraform for infrastructure automation.
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Experience working with Databricks for data engineering and analytics workflows. Experience implementing and optimizing data solutions on Azure, including Azure Data Factory, Azure Synapse Analytics, and Azure Databricks.
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A solid foundation with at least 8 years of experience in IT, with 3+ years dedicated to hands-on data engineering using Databricks. Adeptness with Terraform, Azure DevOps, and other tools essential for modern data engineering.
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Minimum of 5 years of hands-on skills in Python, Apache Spark, Hive, Cassandra, Snowflake, Elastic, Lucene, DataBricks, Hadoop, Redshift, Scala, Java, or other relevant technologies in data engineering.
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We are seeking a proactive and hands-on Data Manager to lead our Data Engineering team within the Data Department. Build and nurture relationships with data and other engineering leaders to ensure alignment on large-scale projects and identify opportunities for improvement, proactively communicating challenges and roadblocks.
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Define and enforce best practices for data engineering and DevOps processes. Data Modeling: Develop and implement data models based on business requirements, including 3NF, Dimensional, and Data Vault models.
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Broad hands-on experience in AWS Cloud serverless components and ability to build and maintain cloud data processing systems (i.e. ECS, EKS, S3, Lambda, SQS/SNS, PostgreSQL, Dynamo, Sagemaker, Athena, etc.
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Extensive expertise as a hands-on technical leader across the full Cisco Voice portfolio - CUCM, Unity, Contact Center, Voice Gateways, etc. This is a hands-on leadership role that combines strategic direction with deep technical acumen across the voice/unified communications domain.
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The ideal candidate will have 5 + years hands-on experience in the life sciences industry, integrating and optimizing Aveva OSI PI systems. Minimum of 6 years of hands-on experience with OSI PI systems in the life sciences industry.
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Hands-on experience with data warehousing, data lakes, and analytics platforms (e.g., Snowflake, Redshift, BigQuery). If you are passionate about leveraging data to drive meaningful insights and have a proven track record in data engineering, analytics, and leadership we invite you to join us on this exciting journey.
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