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3+ years of experience with distributed data/computing tools (MapReduce, Hadoop, Hive, EMR, Kafka, Spark, Gurobi, or MySQL) The Machine Learning Experience (MLX) team supports an internally hosted Model Training ecosystem running critical infrastructure for machine learning and data analysis across Capital One. You will get an opportunity to collaborate on the 1000's of models being trained on the platform by 4000+ users and dive deep into the unique engineering challenges we have around compute, data access, security and scale.
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The ideal candidate will be having sound knowledge of big data ecosystem esp. Spark and handling massive data sets. The ideal candidate will be having sound knowledge of big data ecosystem esp.
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Hands on in Hadoop Ecosystem (Kafka, Spark, Scala, Hive) is a plus. Site reliability engineering (SRE) fuses the software engineering and operations disciplines. Knowledge/Experience in Core Java, GoLang, Rust,Python.
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Solid understanding of distributed computing principles and big data technologies such as Hadoop, Spark, or Hive. Experience with cloud-based data platforms and services, such as AWS, Azure, or Google Cloud Platform.
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We integrate our functionalities deeply into Apache's big data ecosystem (Spark, Hive Presto), and build world-class data security & privacy framework to support high efficient and seamless data life cycle management.
$224,000 - $410,000 a yearFull-timeExpandApply NowActive JobUpdated 10 days ago - UpvoteDownvoteShare Job
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Deep knowledge in Hadoop ecosystem, like HDFS, Hive, MapReduce, presto etc. Expert in multiple distributed technologies (e.g. Spark/Storm, Kafka, Key Value Stores, Caching, Solr , Druid, etc.
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Python ecosystem preferred, R will be acceptable, machine learning libraries & frameworks (e.g. TensorFlow, PyTorch, scikit-learn) and familiar with data processing and visualization tools (e.g., SQL, Tableau, Power BI.
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Demonstrably deep experience with relevant ‘big data’ processing either via Spark or through a modern MPP database like Snowflake or Redshift at TB to PB scale. Across the healthcare ecosystem, we’re helping our clients unlock critical insights to track detailed patient behaviors and treatment patterns, identify gaps in care, address unmet patient needs, and reduce the global burden of disease.
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Design and develop data processing pipelines in the Big Data ecosystem using Spark, Flink, Kafka, Presto, etc. Experience in developing near real-time data processing solutions using Kafka, Flink, Spark Streaming.
$121,500 - $188,500 a yearFull-timeExpandApply NowActive JobUpdated 3 days ago - UpvoteDownvoteShare Job
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Design and implement distributed data processing pipelines using Spark, Hive, Python, Airflow, and other tools and languages prevalent in the Hadoop ecosystem. Explore and build proof of concepts using open source NOSQL technologies such as HBase, DynamoDB, Cassandra and Distributed Stream Processing frameworks like Apache Spark, Kafka stream.
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Knowledge on Leveraging Hadoop ecosystem knowledge to design, and develop capabilities to deliver our solutions using Spark, Scala, Python, Hive, Kafka, and other things in the Hadoop ecosystem.
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Expertise and demonstrated experience in Hadoop ecosystem components: HIVE, HDFS, SPARK. Expertise and demonstrated experience in Hadoop ecosystem components: HIVE, HDFS, SPARK. UI/UX development experience strongly preferred.
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You will have an opportunity to work on the service layer written in Go/Python, debug Jupyter issues/conda environments, support DAG data model migrations, and build the Spark/Dask/Ray compute infrastructure to support the future of AI and ML at Capital One.
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Leverage analytic tools and platforms in support of data evaluation and management activities, e.g. Tableau, Dataiku, Snowflake, Hadoop, Spark, SQL to evaluate data coverage, quality, interoperability, and fit within Pfizer's existing data ecosystem.
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Familiarity with big data technologies such as Apache Spark, Hadoop, etc. You will work on features that touch all Apple Services Engineering Commerce services — from account management, application, music, and video purchase / download / entitlement, subscription management, and a lot of new features coming to the Apple product ecosystem.
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