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Experience with data processing frameworks/tools/platform such as Databricks, Apache Spark, Kafka, Flink, AWS cloud services for batch processing, batch streaming and streaming. Experience deploying Machine Learning solutions on cloud platforms (e.g., AWS, Azure, or Google Cloud Platform.
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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 distributed computing frameworks, such as Apache Hadoop and Apache Spark. Understanding of machine learning concepts and experience with ML platforms like Azure Machine Learning or TensorFlow.
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Apache Kafka, Apache Flink, Apache Spark, Trino (Presto), Apache Airflow/Dagster, Apache Superset, AWS S3, Snowflake, Amplitude, CDP (e.g. Segment.com) The adjacent areas of major focus are Machine Learning Infrastructure and workflow, Experimentation Platform, Knowledge Graphs and various Data Science and Analytics related tooling.
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Exposure to AWS cloud services and running Apache Spark applications. In this role, you'll build and deploy machine learning frameworks, accelerating development to deployment timelines and supporting data scientists in their projects.
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We are currently seeking a Data Technical Lead (Apache Spark, Pyspark and Databricks) to join our team in Dallas, Texas (US-TX), United States (US). In-depth knowledge of Apache Spark, Pyspark, and Databricks architecture.
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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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The ideal candidate will have extensive experience in Python and Apache Spark , along with a deep understanding of high-performance, parallel, and distributed computing to scale machine learning solutions.
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Developing and deploying Spark/Databricks jobs with enterprise tool stacks like Jenkins / GitHub Actions. POC for building an embedding store leveraging autoencoders Build frameworks to fast-track model development to deployment lifecycles Modularize the entity-based feature engineering code base on Hadoop to leverage big data technologies such as Pyspark and Spark SQL.
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We are hiring a talented Senior Data Engineer in roles related to building cloud-based data pipelines for machine learning, data processing with Apache Spark, and database development.
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Extensive experience with Azure Databricks, Apache Spark, and Big Data technologies. Databricks Certified Associate Developer for Apache Spark. Integrate Azure Databricks with other Azure services such as Azure Data Lake, Azure Synapse Analytics, and Azure Machine Learning.
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Examples include MPP data warehouses like Snowflake and Amazon Redshift and all-in-one Apache Spark platforms like Databricks. Examples include Azure Machine Learning Studio, Google’s Vertex AI, IBM Watson Studio, Amazon SageMaker and open-source tools like Kubeflow.
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Much of our work contributes to innovative research in the fields of sensor science, signal processing, data fusion, artificial intelligence (AI), machine learning (ML), and augmented reality (AR.
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Design and implement data-driven solutions using Microsoft Azure’s tools and services, such as Azure Machine Learning, Azure Databricks, Azure Data Factory and Azure databases or Fabric implementation.
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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. You will gain expertise in cloud platforms, data engineering, data analytics, data science, and machine learning.
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