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More than 10,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark, Delta Lake and MLflow.
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Represent the security engineering discipline throughout the organization, having a powerful voice to make us more data-drivenRepresent Databricks at academic and industry conferences & eventsWhat we look for:5+ years of experience in Data Security or related areas and expertise in two or more of the following.
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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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Represent the security engineering discipline throughout the organization, having a powerful voice to make us more data-drivenRepresent Databricks at academic and industry conferences & eventsWhat we look for:9+ years of experience in Data Security or related areas and expertise in two or more of the following.
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Stay abreast of the latest advancements in Databricks and other relevant technologies to continually enhance data engineering practices. · Hands-on experience with big data technologies, including Apache Spark and Databricks Delta Lake.
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Security Engineering is an integral part of Trust & Safety and has a critical role to play in keeping customer data from bad actors. Customers trust Databricks with their most valuable data and Trust & Safety has the mission to build the most trusted data analytics and ML platform in the world.
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8+ years of professional data engineering or software engineering experience, with a focus on heterogeneous, large-scale data processing for machine learning pipelines. Stay up to date with emerging technologies, best practices, and industry trends in data engineering and software development.
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The technology stack of this CSR data product build is Azure SQL Datawarehouse, Python, Databricks, and Azure Datalake at a high level. -Experience with Azure Data Factory, Databricks, or Mlflow.
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Strong technical proficiency to contribute to data engineering task in an AWS and Databricks ecosystem. You must be passionate about data management with a solid background in AWS, Databricks, Python, Trino/Starburst, Databases and SQL.Design, develop, monitor, and maintain data pipelines in an AWS Gov Cloud ecosystem with AWS, Databricks, Delta Lake and Trino as the underlying platforms.
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Significant experience in software engineering and data engineering roles, with a focus on snowflake and Databricks. Design, construct, install, test and maintain highly scalable data management systems, specifically focusing on Snowflake and databricks platforms.
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Exp designing and delivering data engineering solutions with Databricks Exp in Azure Databricks Experience in Data Engineer in zure Big Data Environment Expertise in ETL tools i.e. (Infomatica, Talend, SSIS, Data Stage) Expertise in Implementing Data Warehousing Solutions Exp with working in Agile (KANBAN, Scrumban) environment Exp in zure stack ( zure Data Lake, zure Data Factory) Exp in Scala or Python, T-SQL.
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Work with a team with respect to discovery, design and delivery of data models, data governance models, data migration plans, data warehouse design and reporting components of applications using GCP, Databricks and Microsoft tools.
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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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Experience implementing and maintaining data platform core services (data lake, data integration, data warehouse) and configuring and integrating infrastructure and PAAS (e.g., Azure Data factory, Informatica cloud services, Databricks, Data gateway and Qlik Sense, Power BI.
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Hands-on experience working with Azures PaaS offerings such as Purview, Databricks, Azure Data Lake Storage, Data Factory as well as monitoring of the environments. Implement data engineering, ingestion, and curation functions on Azure cloud using Azure native programming.
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