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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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8+ years experience demonstrating an established track record in Data Engineering and Machine Learning; experience with GPUs as well as expertise in tuning and analyzing Apache Spark workloads.
$176,000 - $333,500 a yearFull-timeExpandApply NowActive JobUpdated 4 days ago - UpvoteDownvoteShare Job
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Expertise in Spark Streaming, Apache Airflow, Cloud (Azure preferred), and on-premises open-source data systems. Design and implement high-performance data processing pipelines using Spark Streaming, Databricks, Snowflake and other technologies to ensure data security and privacy.
$215,000 - $322,250 a yearFull-timeExpandApply NowActive JobUpdated 4 days ago - UpvoteDownvoteShare Job
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This architecture includes an Databricks, Microsoft Azure platform tools (including Data Lake, Synapse), Apache platform tools (including Hadoop, Hive, Impala, Spark, Sedona, Airflow) and data pipeline/ETL development tools (including Streamsets, Apache NiFi, Azure Data Factory.
ExpandApply NowActive JobUpdated 6 days ago - UpvoteDownvoteShare Job
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Strong proficiency in Java Stack programming with expertise in the Spring Boot framework, Apache / Tomcat, Apache Maven, Apache Spark, API Concepts and Methodologies, API Security, Camunda, Cloud Automation, Cloud Computing, Cybersecurity, Data Engineering, Database Design (Physical), DevOps, Java and Related Technology, JavaScript, Linux.
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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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Founded in 2013 by the original creators of Apache Spark, Databricks has grown from a tiny corner office in Berkeley, CA to a global organization with over 1500 employees. Security Engineering is an integral part of Trust & Safety and has a critical role to play in keeping customer data from bad actors.
Full-timeExpandApply NowActive JobUpdated 24 days ago - UpvoteDownvoteShare Job
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Including data processing using Apache Spark, Delta Lake, Unity Catalog, and MLflow. The Director of Data Engineering, owns the data strategy, architecting the right data platform, and serving analytics-ready data products to meet business needs.
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Familiarity with big data technologies such as Apache Spark, Hadoop, etc. You will work on features that touch all Apple Services Engineering Commerce services — from account management, application, music, and video purchase / download / entitlement, subscription management, and a lot of new features coming to the Apple product ecosystem.
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Expertise in building robust data engineering frameworks, Azure cloud, python/JAVA/Scala, and Data bricks (Apache Spark). Knowledge of Hyperscale SQL Server and Snowflake Data engineering best practices around data modelling, evaluation, deployment, and governance.
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Strong programming skills with one or more languages such as Python, PySpark, Apache Spark, Java, JavaSript, C, C. The Director, Data & ML Engineering willl be responsible for both data engineering and MLOps engineering teams to support data science applications.
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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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Hands-on experience with big data technologies, including Apache Spark and Databricks Delta Lake. · Stay abreast of the latest advancements in Databricks and other relevant technologies to continually enhance data engineering practices.
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Represent the security engineering discipline throughout the organization, having a powerful voice to make us more data-driven. 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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Oversee the entire lifecycle of data engineering projects: build scalable, reliable, and high performing data ingestion pipelines that collect, transform, integrate, and load structured, semi-structured, and unstructured data from a variety of sources, such as transactional data, data warehouse, and data lakes.
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