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Experience with Apache Airflow (AWS MWAA) and Apache Spark required. Experience with Apache Airflow (AWS MWAA) and Apache Spark required. Familiarity with one or more Python data visualization libraries (Bokeh, Matplotlib, Seaborn.
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Experience with Big Data technologies like Snowflake, Databricks, Spark is preferred. Required Skills Include: Core Java, J2EE, Spring Boot, Python, REST services, Swagger, Apache Camel, Spring Batch, Kafka/MQ, MongoDB.
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Familiarity with distributed computing frameworks, such as Apache Hadoop and Apache Spark. Familiarity with data integration tools (e.g., Apache Kafka, Apache Nifi.
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Proficiency in Microsoft Azure cloud services, including Azure Data Factory, Azure Databricks, Azure SQL Database, Azure Data Lake, etc. Knowledge of Azure DevOps for CI/CD (Continuous Integration/Continuous Deployment.
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Proficiency with the following technical capabilities: Apache Spark, Databricks, Azure Data Factory, Azure Machine Learning Studio, SQL, Docker and/or Kubernetes. Proficiency with the following technical capabilities: Apache Spark, Databricks, Azure Data Factory, Azure Machine Learning Studio, SQL, Docker and/or Kubernetes.
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Experience with building data pipelines in getting the data required to build and evaluate ML models, using tools like Apache Spark, AWS Glue or other distributed data processing frameworks.
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This is a fully remote role for people who are able to support Eastern Time Zone core hours. Bachelors degree in Computer Science, Computer Information Systems, IT, Software Engineering, Data Science or Analytics or some related field.
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Experience with cloud computing services (e.g. Microsoft Azure, Amazon Web Services) and associated large-scale data analytics (e.g. Databricks, Apache Spark) is desirable. Experience with cloud computing services (e.g. Microsoft Azure, Amazon Web Services) and associated large-scale data analytics (e.g. Databricks, Apache Spark) is desirable.
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Our infrastructure is built on EMR, Hadoop, Hive, Spark, Flink, Hudi, Apache Ranger, Sqoop, Airflow, Presto, Kafka, Secor and Aurora/ MySQL/ Vitess. Our infrastructure is built on EMR, Hadoop, Hive, Spark, Flink, Hudi, Apache Ranger, Sqoop, Airflow, Presto, Kafka, Secor and Aurora/ MySQL/ Vitess.
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Required technical skills: Python, SQL (PostgreSQL preferred), NoSQL (Elastic and or any document database preferred), Messaging system (SQS/Kafka), Distributed Data Processing system (Apache Spark), CI/CD pipeline (Azure devops preferred), Restful API (Fast API/Flask preferred), Container technology like docker or AWS Elastic container service.
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Solid experience with data pipeline tools and workflow engines, e.g. DBT, Apache Airflow, AWS GLUE, Spark. Solid experience with data pipeline tools and workflow engines, e.g. DBT, Apache Airflow, AWS GLUE, Spark.
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Develop production ready software using web, cloud and distributed technologies including Apache Spark, Hadoop, Microservices, and CI/CD cloud deployment. Developing production ready software using web, cloud and distributed technologies including Apache Spark, Hadoop, Microservices, and CI/CD cloud deployment.
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Design and develop solution architectures to scale backend systems using Python, Java, Spring Boot and rest specifications. Designing and developing solution architectures to scale backend systems using Python, Java, Spring Boot and rest specifications.
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Experience maintaining Cloudera Hadoop infrastructure such as HDFS, YARN, Spark, Impala and edge nodes. Experience with Apache NiFi, Apache Kafka and Talend for data ingestion. Experience with Apache Airflow for scheduling / job orchestration.
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Experience in building cloud native applications using AWS services like S3, RDS, CFT, SNS, SQS, Step functions, Event Bridge, cloud watch etc.,- Experience with building data pipelines in getting the data required to build, deploy, and evaluate ML models, using tools like Apache Spark, AWS Glue or other distributed data processing frameworks.
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