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Provide technical expertise and guidance on the selection, and hands-on implementation, and optimization of big data platforms, tools, and technologies across multiple cloud environments (e.g., AWS, Azure, GCP, Snowflake, etc.
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Hands-on experience on Oracle, SQL Server, COSMOS, MySQL, PostgreSQL, MongoDB, Cassandra. Analyse on premises/ Azure transactional database environments such as Oracle, SQL Server, COSMOS, MySQL, PostgreSQL, MongoDB, Cassandra evaluate and plan migrations to Azure.
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This engineer will need hands on experience with Terraform, Kubernetes, Jenkins, Ansible, AWS, Docker, CICD, DevOps, etc. experience with: Terraform, Kubernetes, Jenkins, Kafka, Github, and configuration management tools such as Ansible.
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Hands on experience on design solutions using open-source technologies for containers, such as Docker, Kubernetes, Grafana, Open Telemetry, Prometheus, Helm, and Kafka. Hands-on experience with IaC tools such as Terraform, and DevOps tools such as Jenkins, Harness, Artifactory.
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Hands-on experience with the Kubernetes, Kafka, Airflow, Python, Ubuntu Linux, AWS (or similar cloud platforms) The Data Architect will use their skills and experience of technologies including Kubernetes, Kafka, Airflow, Linux, AWS and Python to spearhead the evolution and enhancement of their existing data infrastructure, as they scale up the adoption of their software as a medical device product.
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Practical hands on experience with: Building large-scale event driven systems and relevant services (Kafka, Confluent, RabbitMQ) and streaming based data pipelines. Working, hands on, knowledge of modern Data Pipeline tools (DBT, Snowflake, Airflow) and Data visualization tools (Looker.
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Hands-on experience with Kafka or other live streaming technology is highly desired. 3+ years hands-on experience working with large, structured/unstructured datasets using partitioned cloud storage architecture using query engines such as Spark, Delta Lake.
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Extensive hands-on experience implementing data migration and data processing using AWS services: VPC/SG, EC2, S3, Autoscaling, CloudFormation, Lake Formation, DMS, Kinesis, Kafka, Nifi, CDC processing Redshift, Snowflake, RDS, Aurora, Neptune, DynamoDB, CloudTrail, CloudWatch, Docker, Lambda, Spark, Glue, Sage Maker, AI/Client, API GW, etc.
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Strong hands-on experience with IBM Event Streams, Apache Kafka, IBM WebSphere MQ, Red Hat Ansible, and Red Hat OpenShift. In this role, you will leverage your hands-on production experience and deep understanding of the Kafka architecture to ensure the smooth operation of our messaging infrastructure.
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Minimum of 5 years of hands-on skills in Python, Apache Spark, Hive, Cassandra, Snowflake, Elastic, Lucene, DataBricks, Hadoop, Redshift, Scala, Java, or other relevant technologies in data engineering.
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The ideal candidate must be hands-on and will have a strong background in data engineering, with proven experience in developing and deploying large-scale data solutions in a cloud environment, must be metrics-focused and obsessed with Cloud native and DevOps technologies.
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Advanced skills in Node.js, TypeScript, Apollo GraphQL, MongoDB, and front-end development with Angular, HTML5, CSS, SASS(5+ years of working/hands on experience on a development project.
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Azure, AKS (Azure Kubernetes Services), Architecture Principle Design, Azure - Migration, Azure Administration (IaaS), Cassandra, Cloud Migration, Kafka, Migration Design, Migration Planning, MongoDB.
$70 - $75 an hourExpandApply NowActive JobUpdated 11 days ago - UpvoteDownvoteShare Job
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Solid understanding of on-prem and hybrid cloud infrastructure, container orchestration, MongoDB, Kafka, and IBM mainframe DB2 (preferred) - Deep understanding and hands-on experience with networking principles and technologies (Cisco, Arista, AVI.
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3+ years of experience as a hands-on Data Engineer/Architect including ETL jobs, data pipelines, and Big Data analytics. Experience in technologies such as Hadoop, Spark, Kafka, Redis, Cassandra, Pandas, Dask, Airflow, Apache Beam, MongoDb, Hive, Impala, Hazelcast, Athena, Presto.
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