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Distributed Compute: Spark/Databricks. Database / Warehouse: PostgreSQL, DynamoDB, Databricks Unity Catalog. Experience with AWS + Databricks Unity Catalog / Model serving.
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Apache Spark-> Databricks Certified Developer: Apache Spark 2. Apache Spark-> Cloudera - CCA Spark and Hadoop Developer (CCA175) Product SME for Confluent Kafka, Apache Spark, Talend.
ExpandApply NowActive JobUpdated 4 days ago - UpvoteDownvoteShare Job
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Snowflake or Databricks certifications and/or hands-on-keyboard experience. Strong proficiency in programming languages such as Python, Java, or Scala, with expertise in data processing frameworks and libraries (e.g., Spark, Hadoop, SQL, etc.
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Demonstrate expertise and hands-on experience with Apache Spark tools such as Google Dataproc, Amazon EMR, and Databricks. 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 5 days ago - UpvoteDownvoteShare Job
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Integrating Databricks with other AWS products such as SNS, SQS, and MSK.What we are looking for: Bachelor's or master's degree in computer science, Information Technology, or a related field. Strong technical proficiency to contribute to data engineering task in an AWS and Databricks ecosystem.
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Experience in Apache Spark, Databricks or similar distributed processing systems. Implement data pipelines using Apache Spark/Databricks. You will primarily be working on Apache Spark/DataLake/Airflow (in Python) and other cloud-based technologies.
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Design, build & deploy the data pipelines using EMR, EMR studio, Databricks, Spark, Pyspark and Shell ScriptingDesign the Data replication systems and integration systems by evaluating various products and deciding on the better technology that meets the corporate objectives.
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And familiarity with relevant frameworks and libraries for AI and big data, such as TensorFlow, Apache Spark, Databricks or Snowflake. Candidate should have in-depth experience in designing and implementing enterprise-wide cloud data and analytics platforms such as Hadoop, Spark, and Kafka for handling large volumes of document data efficiently and extracting insights effectively.
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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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Familiarity with Hadoop, Spark, and Cloud databases (Snowflake, BigQuery, Redshift, Databricks) and stream processing frameworks (Kafka, Kinesis). Familiarity with Hadoop, Spark, and Cloud databases (Snowflake, BigQuery, Redshift, Databricks) and stream processing frameworks (Kafka, Kinesis.
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You will assist clients in choosing a platform, defining their data needs and migrating them to a modern cloud data environment using cloud providers such as Azure, Google Cloud Platform, Amazon Web Services, Snowflake, Databricks or Teradata.
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Utilize Spark, Databricks, Tensorflow, and Python to handle data extraction, analysis, and model development. Proficiency in Python development, Tensorflow, and Databricks. Minimum of 3+ years of experience in data science with a focus on machine learning, deep learning, computer vision, NLP, and Spark.
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Experience in executing Scala/Spark code using Databricks framework." Risk IT-Apache Spark/Scala/Big data. Title: Bigdata Consultant (Apache Spark + Scala + Java) Mid exp level Developer : Java/Scala, Sql, Spark.
ExpandApply NowActive JobUpdated 7 days ago - UpvoteDownvoteShare Job
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2+ years of experience building highly scalable data solutions using Snowflake, Hadoop, Spark, Databricks 2+ years of experience working in cloud environments (AWS and/or Azure). 2+ years of experience building highly scalable data solutions using Snowflake, Hadoop, Spark, Databricks 2+ years of experience working in cloud environments (AWS and/or Azure.
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Spark, Scala, and Databricks. Experience in data product development at enterprise scale, including on cloud platforms, Spark or other big data ML systems, and comfort using Jira, Slack, Teams, and other collaboration tools.
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