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We utilize a plethora of open source and industry accepted technologies for our big data problems such as Apache Spark, Apache Storm, Amazon Web services, and Apache Kafka. e Apache Kafka or Kinesis or RabbitMQ)Has worked with systems processing large amounts of data Has worked with partner data scientist, data analysts and other domain experts to understand their needs and be able to develop solutions Experience working with AWS or similar cloud platform technologiesAuthorized to work in the United States with or without sponsorship.
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The VP, Engineering will lead scaling the global data and software engineering teams in managing the foundational enterprise data platform and creating tools and frameworks that enhance data collection and processing, analytics and artificial intelligence, data dissemination, and governance.
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Familiarity with big data processing frameworks such as Apache Spark and experience with programming languages like Python, Scala, or SQL. Continuously evaluate new cloud technologies and data processing frameworks to enhance the feature store's capabilities, including integration with Azure-based services like Azure Data Factory, Azure Databricks, and others.
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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.
$97,000 - $150,000 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Minimum 2 years of experience with large-scale data processing platforms such as Spark, EMR, and/or HPC computing experience with e.g. Apache Aurora, Slurm. Minimum 5 years of experience with ETL/ELT design and development using tools like IICS, AWS Glue, Talend, Databricks, Oracle Data Integrator (ODI) or equivalent.
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Familiarity with advanced data processing technologies such as Apache Spark or Kafka. - Experience with data reporting (e.g., Microstrategy, Tableau, Looker) and data cataloging tools (e.g., Alation.
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Data processing programming using SQL, DBT, Python, and similar tools. Experience in implementing data pipelines for both streaming and batch integrations using tools/frameworks like Azure Data Factory, Glue ETL, Lambda, Spark, Spark Streaming, etc.
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Architect and develop large-scale, distributed data processing pipelines using technologies like Apache Spark, Apache Beam, and Apache Airflow for orchestration. Proven expertise in Apache Spark, Apache Beam, and Airflow, with a deep understanding of distributed computing and data processing frameworks.
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Demonstrated experience with big data processing systems and distributed computing technology such as Databricks, Spark, Sagemaker, Kafka, etc. Tags: Boston Dynamics, Boston Dynamics AI Institute, BDAI, robots, robotics, AI, artificial intelligence, ML, machine learning, Python, Python, SQL, SQL, NoSQL, AWS, Spark, Kafka, ETL, Sagemaker, Airflow, Redshift, Snowflake, Senior Data Engineer, Data Engineer, data warehousing, C.
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Familiarity with big data technologies such as Apache Spark, Hadoop, or Kafka for processing large-scale datasets. Optimize ETL processes for performance and scalability utilizing AWS Glue, EMR, Step Functions, and Lambda to ensure efficient data processing and timely delivery.
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With distributed computing frameworks like Spark for big data processing. We are seeking a talented Data Science Engineer to join our team and contribute to the development and implementation of advanced data solutions using technologies such as AWS Glue, Python, Spark, Snowflake Data Lake, S3, SageMaker, and machine learning (M/L.
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Extensive experience optimizing data streaming, processing and storage utilizing tools such as Amazon EMR, Apache Kafka, Apache Spark, Amazon S3, and Data Lakehouse technologies.
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Experience with big data technologies and data pipelines such as Hadoop, Spark, Airflow, etc. The Senior Data Engineer will be responsible for monitoring and analyzing data accuracy, automating data collection and processing, data modeling, building data visualization tools, dashboards, and reports.
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At least 3 years of experience working with data lakes, data warehousing, and/or database systems, including processing frameworks like Spark, Hadoop, Presto, etc. Contribute to data platform that supports batch and real-time data sets using modern data processing frameworks.
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Consumer analytics and near real time data processing (Spark Streaming); Implement data-driven solutions in a production environment using tools such as Spark and Kafka.
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