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5+ years working experience with key open source big-data projects as a contributor or committer including Apache Spark, Apache Flink, Trino, Apache Kafka, Apache Hive, Apache Arrow, Apache Hadoop, Delta Lake, Apache Iceberg.
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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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Ideally you have an in-depth understanding of streaming data technologies like Amazon Kinesis or Apache Kafka, and experience with open-source data processing frameworks like Apache Spark, Apache Flink, or Apache Storm.
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This implies, among others, writing software in Scala/Java, building data pipelines (Apache Spark, Apache Kafka), integrating with third-party applications, and interacting with cloud APIs (AWS, Azure, CloudFormation, Terraform.
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O Programming Language: Python, R, SQL, Java, Scala, Pyspark/Apache Spark, Shell scripting. Use programming languages including but not limited to Python, R, SQL, Java, Scala, Pyspark/Apache Spark, Shell scripting.
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Databricks Certifications: Certifications such as Databricks Certified Associate Developer for Apache Spark or Databricks Certified Professional Data Scientist can demonstrate expertise and enhance job prospects.
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Machine learning will cover everything from data science, Artificial intelligence, business analytics, deep learning, and computer science. Difference between Machine learning, Artificial Intelligence and Deep learning.
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Hands-on experience with the Databricks platform and Apache Spark. Proficient in programming languages such as Python, Scala, or SQL. In-depth knowledge of Apache Spark and its ecosystem.
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Establish and set up model life cycle management with tools like MLFlow , etc · Developing and deploying Spark/Databricks jobs with enterprise tool stack including Jenkins, GitHub Actions · Deployment utilizing containerization solutions like Docker and Kubernetes · Experience with AWS cloud services and running Apache Spark applications.
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Experience in Installing, Configuring, tuning, and monitoring Apache Spark and Airflow on Kubernetes. Strong knowledge of Apache Spark and Apache Airflow architecture.
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We are seeking builders for our Amazon MSK service, a fully managed service that makes it easy for customers to build and run applications that use Apache Kafka to process streaming data. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.
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At least 4 years of experience in data engineering working with Big Data Technologies: Apache Spark, Pyspark, Hadoop, and DataBricks with delta lake. Experience with Apache Airflow, Hive, Snowflake, Kafka, Python.
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Leverage AWS cloud services like S3, Kinesis, Redshift, EMR, Glue, Lambda, and others, in combination with Data Lakehouse platform/Apache Spark Integration for advanced data processing and analytics.
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Technical Proficiency: Expertise in Python, machine learning tools (Hadoop, TensorFlow, Apache Spark), and deep learning frameworks. Your innovative approach will harness machine learning and artificial intelligence to deliver impactful solutions, enabling customers to make informed, data-driven decisions through prototypes and scalable production systems.
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Demonstrated expertise in machine learning, deep learning, and NLP, with a strong portfolio in large language models like GPT-3, BERT, and RAG.Advanced proficiency in Python, with hands-on experience in ML libraries and frameworks such as TensorFlow and PyTorch.
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