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8+ years experience with big data technologies including: Apache Spark, Apache Hudi, Hive, Airflow, Trino, and Databricks. 8+ years experience with big data technologies including: Apache Spark, Apache Hudi, Hive, Airflow, Trino, and Databricks.
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Experience working with Apache Spark and Databricks environments. Experience working with Apache Spark and Databricks environments. Implement best practices for infrastructure as code (IaC) using tools like Azure Resource Manager templates, Terraform, or Bicep.
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Active contributor to one or more infrastructure software such as Snowflake, CockroachDB, Oracle RDBMS, Google BigQuery, Spanner, Redshift, Aurora, Cosmos DB, MySQL, PostgreSQL, Hudi, Delta Lake, Iceberg, Ray, Spark, Flink, Kafka, Redis, ElasticSearch, etc.
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Strong understanding of Azure infrastructure services, such as Virtual Machines, Storage Accounts, Azure Data Lake Storage, Azure Synapse Analytics, and Azure Databricks. Design and deploy scalable and secure Azure infrastructure solutions for our data platform, including Databricks, Azure Data Lake Storage, Azure Synapse Analytics, and other related services.
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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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3+ years of Experience with Apache Spark, Kafka, Pig, Oozie, Flume, and Streaming and similar distributed computing technologies. Strong mathematical background with strong knowledge in at least one of the following fields: statistics, data mining, statistics, operations research, econometrics, and/or information retrieval (image processing (OpenCV) and natural language processing is a plus.
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Big Data Technologies: Distributed Computing: Tools like Apache Spark, Hadoop. Big Data Storage: Technologies such as HDFS, Apache Cassandra, or cloud storage solutions. Stream Processing: Tools like Apache Kafka, Apache Flink, or AWS Kinesis.
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Experience with data processing technology (Apache Spark etc.) HPC Nvidia GPU GenAI LLM MLOps AIML NLP. 5+ years of experience with Azure and/or GCP/GKE, as well as experience building complex infrastructure programmatically with IaC tools (Terraform/Ansible etc.
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Must have experience with data analysis programming languages (e.g., SQL, Python & Apache Spark, SAS & R) Learn and demonstrate mastery across a wide variety of data engineering activities, including data warehousing, master data management, data cataloging, system integration, data streaming, data visualization, data analysis, and data ops.
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Highly proficient with Apache Spark, Scala/ Python and PySpark. Expertise in Apache Spark along with Spark streaming & Spark SQL. Developing and optimizing Spark-based data processing applications on the AWS Databricks platform.
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Partner with the Data Science team and introduce new capabilities to optimize the Databricks usage for performance and cost. This team owns the set up, ongoing administration and governance of the Lakehouse platform, which is critical in driving the broad adoption of Databricks within an organization.
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Python, Apache Airflow, Apache Spark, PostgreSQL, Presto SQL, Snowflake SQL, Confluence, JIRA. Python, Apache Airflow, Apache Spark, PostgreSQL, Presto SQL, Snowflake SQL, Confluence, JIRA.
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You will be in a customer-facing role that requires deep hands-on expertise in Apache Spark™ and data engineering, along with a variety of knowledge of the big data ecosystem. You will be part of the Professional Services team and work with clients on short to medium term customer engagements solving their big data problems using Apache Spark and the Databricks platform.
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Experience with high speed distributed computing frameworks such as AWS EMR, Hadoop, HDFS, S3, MapReduce, Apache Spark, Apache Hive, Kafka Streams, Apache Flink etc. Experience with high speed distributed computing frameworks such as AWS EMR, Hadoop, HDFS, S3, MapReduce, Apache Spark, Apache Hive, Kafka Streams, Apache Flink etc.
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Experience with Big Data frameworks such as Hadoop, Apache Spark, Apache Beam. Experience with Big Data frameworks such as Hadoop, Apache Spark, Apache Beam. Experience with Python frameworks (e.g. Django, Flask, Bottle.
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