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Experience with Big Data Technologies (Hadoop, Hive, HBase, Pig, Spark, Apache Druid) such as S3, EC2, and EMR (Spark) etc. Lead the transformation of a peta-byte scale batch-based processing platform to a near real-time streaming platform using technologies such as Apache Kafka, Cassandra, Spark and other open source frameworks.
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Prophecy is used by customers to develop data engineering workflows on Apache Spark, Apache Airflow, and Data Warehouses. Address complex customer issues related to Apache Spark and Data Warehouses.
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Work with Hadoop ecosystem tools for real-time & batch data ingestion, processing and provisioning such as Apache Spark and Apache Sqoop. Job Role and Responsibility: Design and build high performing and scalable data pipeline platform using Hadoop, Apache Spark, MongoDB and object storage architecture.
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Experience building robust and scalable data integration (ETL) pipelines using Airflow, SQL, Python, and Spark. Job Title: Data Science Engineer. Specialized Area: Data Science.
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Solid experience with big data technologies (Apache Spark, Hadoop, Kafka) and cloud services (AWS, Azure) related to data processing and storage. Design and implement scalable and reliable data pipelines to ingest, process, and store diverse data at scale, using technologies such as Apache Spark, Hadoop, and Kafka.
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Experience with Big Data Technologies (Hadoop, Hive, HBase, Pig, Spark, Apache Druid) such as S3, EC2, and EMR (Spark). Data Warehousing experience with Oracle, Redshift, Teradata, Snowflake, etc.
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Databricks Certified Associate Developer for Apache Spark Preferred. Utilize ETL tools and frameworks (e.g., Apache Airflow, Talend) to automate data workflows, ensuring efficient data integration and timely availability of data for analytics.
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Big Data Platforms (Apache Spark, Presto, Amazon EMR) Python, SQL, AWS, GCP, Golang, Hadoop, Snowflake, RDBMS, Kakfa, BI Reporting Tools, And Data Science Toolkits.
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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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As a member of our team, you will sharpen your skills on open-source projects such as Apache Spark™, MLflow, and Delta Lake to advocate the Lakehouse paradigm on public cloud providers. Become an expert in, and evangelize Databricks driven open-source projects (Apache Spark™, Delta Lake, MLflow, Koalas) across developer communities through meetups, conferences, and webinars.
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This role requires a deep understanding of big data technologies, particularly Apache Spark, as well as expertise in data engineering and analytics. In-depth knowledge of Apache Spark and Databricks architecture.
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Experience with Apache Spark, Databricks, and standard DS/ML libraries. BS, MS, or PhD degree in Computer Science, Data Science, Economics, Statistics, Mathematics, Physics, Computational Biology or equivalent experience.
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Master's Degree computer science, MIS, or other business discipline and 5+ years of experience in data engineering, with a proven track record in designing and operating large-scale data pipelines and architectures Req.
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Design, implement, and maintain complex ETL pipelines using tools such as Apache Spark, Apache Airflow, and AWS Glue for both batch and near real-time data processing. Expert knowledge of distributed data processing frameworks like Apache Spark, Hadoop, or Flink.
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Data Engineering technologies (e.g., Spark, Hadoop, Kafka) You will gain expertise in cloud platforms, data engineering, data analytics, data science, and machine learning.
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data science apache spark jobs Company: Intuit in ND, France
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