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Solid experience in big data engineering, with working knowledge of Hadoop, Apache Spark, Python, and SQL. A strong background in data engineering with experience in Hadoop, Spark, and Scala is also essential for effective performance in this role.
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Data engineering, database engineering, business intelligence, or business analytics, Master’s degree in Computer Science or related field and 2 years' experience in software engineering or related field, We value candidates with a background in creating inclusive digital experiences, demonstrating knowledge in implementing Web Content Accessibility Guidelines (WCAG) 2.2 AA standards, assistive technologies, and integrating digital accessibility seamlessly.
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Proficiency in data engineering tools and technologies such as Apache Airflow, Spark, Kafka, Hadoop, or similar. - Implement automated data pipelines using tools like Apache Airflow, Spark, Kafka, or similar technologies, ensuring data is processed efficiently and accurately.
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Implementation and tuning experience in the Big Data Ecosystem, (such as EMR, Hadoop, Spark, R, Presto, Hive), design principles) and Data Warehousing (such as Redshift, Teradata, Vertica, schema design, query tuning and optimization) and data migration and integration.
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Experience with big data technologies such as Apache Hadoop, Spark, Kafka, and others. Education and Experience:BS in Computer Science, IT, Software or Computer Engineering, Data Science or Analytics, Mathematics, or a related field.
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We are expanding in data science to provide the best information possible utilizing the latest techniques in Machine Learning (including Deep Learning, Neural network). , statistics, operations research, bioinformatics, economics, computational biology, computer science, mathematics, physics, electrical engineering, industrial engineering.
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Solid experience with big data technologies (Apache Spark, Hadoop, Kafka) and cloud services (AWS, Azure) related to data processing and storage. Design and implement scalable and reliable data pipelines to ingest, process, and store diverse data at scale, using technologies such as Apache Spark, Hadoop, and Kafka.
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We work alongside product teams across MI ES on break-through ideas using tools and techniques spanning the entire spectrum of Data Science, Statistics, Machine Learning, Deep Learning, NLP, Gen AI, Operations Research, Data and Machine Learning Engineering.
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You'll have the opportunity to work with a modern tech stack, including Python, Django, React, TypeScript, Kafka, Terraform, Docker, Kubernetes, and AWS serverless technologies. 30% Platform Engineering (Kafka, Terraform, Docker, Kubernetes, AWS.
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Familiarity with Snowflake, Monte Carlo, RDS, DynamoDB, Kafka, Fivetran, dbt, Airflow, Docker, Kubernetes, EMR, Sagemaker, DataDog, PagerDuty, Atlan, Data Observability tools and Data Governance tools.
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Expertise in data technologies, such as relational databases (e.g., Oracle, PostgreSQL, SQL Server), cloud data lakes / warehouses (e.g., Snowflake, Redshift), data pipelining (e.g., Spark), data streaming (e.g., Kafka), and cloud services (e.g., AWS, Azure.
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Completed BS, MS, or PhD in Computer Science, Mathematics, Statistics, Data Science, Engineering, Operations Research, or other quantitative field. Experience with orchestrating complex workflows and data pipelines using like Airflow or similar tools.
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Working knowledge of dashboard data visualization including: AWS Quicksight, Google Looker, and/or Microsoft Power BI. We are specifically looking for someone who understands the role of the digital analytics director and the critical part that this team will play in scaling the digital transformation at SBNA. This person should be completely at home helping build and grow a team that can both lead in the data collection and syndication, as well as in the insights and analytics necessary to drive growth and improve customer experience.
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Experience in big data technologies such as Spark, Hadoop, or Databricks. Bachelor s or Master s degree in Computer Science, Data Engineering, or a related field. The ideal candidate will be responsible for architecting and overseeing the implementation of Snowflake data warehouses, optimizing performance, ensuring data security, and leading data migration efforts.
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Experience utilizing Microsoft Data & Analytics Services (like Azure Data Lake, Azure Data Factory, Azure Databricks etc) 5 or more years managing Master Data Management delivery for Product, Supplier or location domain using tools like Stibo or IBM Infosphere or SAP.
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hadoop kafka data science jobs Company: Mckesson
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