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Familiarity with data integration techniques, ETL frameworks (e.g., Apache Spark), and workflow management tools (e.g., Airflow). Experience with data engineering tools and frameworks such as Apache Airflow, Apache NiFi, Talend, etc.
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Big Data & Streaming: 2+ years using big data and/or streaming technologies (e.g. Apache Spark, Apache Kafka, Apache Flink) Design, develop and deliver scalable, robust and highly re-usable components using technologies such as Python, Java, AWS serverless (Lambda, Glue), Apache Spark, Apache Kafka and REST.
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Data: Good understanding of data & data processing tools (e.g. Spark, Kafka, SQL), of relational database technologies and of analytics databases (e.g. Redshift, Vertica, Snowflake) Spark, Kafka, SQL), of relational database technologies and of analytics databases (e.g. Redshift, Vertica, Snowflake.
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Proficiency in Apache Spark, including Spark Core, Spark SQL, and Spark Streaming. Use of tools like Apache Airflow, ADF, or Databricks to orchestrate and schedule data workflows.
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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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We use Apache Airflow, Python, and Apache Spark for ETL. For streaming data, we use Apache Kafka managed by a vendor and use Kafka Connect, Kafka Streams, and Spark Streaming for stream processing.
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Apache Spark is a multi-language engine for data engineering, data science, and machine learning that lies at the core of our initiatives. 5+ years experience working in a related system, including ecosystem, Apache Spark and database internal, database systems, data processing or related domains.
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2+ years’ expertise in Databricks, Apache Spark, Snowflake and/or similar data processing frameworks. Databricks Expertise: Optimize data processing by using your expertise in Databricks, develop Spark-based solutions, and enhance data integration and analytics capabilities.
$150,900 - $222,200 a yearFull-timeExpandApply NowActive JobUpdated Yesterday - UpvoteDownvoteShare Job
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Similar Jobs (5) Senior Systems Software Engineer - Performance - Accelerated Apache Spark locations 2 Locations time type Full time posted on Posted 5 Days Ago Senior Systems Software Engineer, C.
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You will work with an engineering team accelerating Apache Spark with GPUs using CUDA and open source libraries. Contributions to major open source projects such as Apache Spark, Apache Kyuubi, Apache Ranger, Apache Iceberg, and Delta Lake. Development experience of Apache Spark Data Sources and connectors.
$216,000 - $333,500 a yearFull-timeExpandApply NowActive JobUpdated Yesterday - UpvoteDownvoteShare Job
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Micro-batch streaming and continuous streaming process in Databricks for high latency, low latency and ultra-low latency of data accordingly by using inbuilt Apache spark modules. Strong hands-on expertise in building Data engineering solutions using Java, Apache Spark and Scala.
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We use various open-source technologies (such as Apache Spark, Presto, Comet, Apache Airflow to name a few) to develop services for Adobe customers and partners. Plus, to be a Committer or PMC Member with the Apache Software Foundation on big data technologies: Apache Spark, Hadoop Stack, Kafka, Comet, Atlas, delta.
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Optional Skills:Angular, Apache Hadoop, Apache Hadoop Administration, Apache Kafka, Apache Spark, Apache Storm, Big Data, Cloud Infrastructure, Docker (Software), Docker Swarm, Innovation, Internet of Things (IoT), JavaScript, Kubernetes, React.
$154,800 - $212,800 a yearFull-timeExpandApply NowActive JobUpdated 2 days ago - UpvoteDownvoteShare Job
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3+ years of experience scaling and operating distributed systems like big data processing engines (e.g., Apache Hadoop, Apache Spark), distributed file systems (e.g. HDFS, CEPH, S3, etc.
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Ideally you have an in-depth understanding of streaming data technologies like Amazon Kinesis or Apache Kafka, and experience with open-source data processing frameworks like Apache Spark, Apache Flink, or Apache Storm.
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