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8+ years of experience in wide variety of distributed systems and frameworks – such as Apache Spark, Kafka, Airflow, Kubernetes, Cassandra, Druid, Snowflake and more. Implement cutting-edge AI/ML techniques, including Generative AI, to boost the efficiency of the platform and data engineering processes.
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Good experience with message driven software patterns (Kafka, MQ) and big data technology ( Hdfs, Hive, Hbase, Spark, Impala, Zeppelin,Jupyter, Cassandra, Elasticsearch, etc.) Experience in AWS Data Analytics platform and related services - S3, AWS Glue, RedShift, Athena, Lake Formation, Lambda etc.
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Experience with large scale enterprise applications using big data open-source solutions such as Spark, Kafka, Elastic Search / Solr and Hadoop, HBase. - Experience or knowledge of basic programming and DB's technologies (SQL, Python, Cassandra, PostgreSQL, AWS Aurora, AWS RDS , MongoDB, Redis, Couchbase, Oracle, MySQL, Teradata.
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As a Sr. Big Data Engineer, you will work with a variety of talented teammates and be a driving force in technical initiatives that will accelerate analytics at Client. We’re looking to expand our Big Data Engineering team to keep pace.
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Java/Python developer on Hadoop/Spark/Big Data platform with AWS experience preferably on EMR, EKS, Glue, Lake Formation. Java/Python Developer that's worked on a Hadoop/Spark/Big Data Platform that has AWS experience (Preferably on EMR, EKS, Glue, Lake formation.
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Experience with Big Data Technologies ( i.e., Hadoop, HDFS , MapReduce, Hive, Pig, Spark, etc. Experience using modern technology ( e.g., Java, Cassandra, Kafka) and design patterns (microservices, APIs , streaming.
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Strong understanding of relational databases (e.g., Oracle, SQL Server, MySQL) and NoSQL databases (e.g., MongoDB, Cassandra), and data integration technologies (e.g., ETL tools, data virtualization, API integrations.
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Knowledge in big data and streaming data architecture (Lambda/Kappa), Understand open-source databases like MySQL, PostgreSQL, etc., and be familiar with No-SQL databases like Cassandra, MongoDB, Elasticsearch (Knowledge with NewSQL database like TIDB, Cockroach or YugabyteDB will be a plus.
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Experience with big data frameworks ( i.e. Hadoop and Spark) This Role will bring a DevOps mindset to enable big data and batch/real-time analytical solutions that leverage emerging technologies.
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Experience in CI/CD pipeline and related open-source tools like GIT/Jenkin/CircleCI/SonarQube knowledge in Terraform/Ansible will be big plus. Use deep hands-on experience in complex system design and data pipeline and architectures, scale and performance, tuning, with good knowledge on Docker and Kubernetes.
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Big Data Framework skills including Spark, Hadoop, Cassandra, Elasticsearch, Docker. The Solution Specialist – Big Data Cloud function is a mid-level position and part of the Solutions Specialist- Software job family.
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Shall have experience diagnosing and troubleshooting large scale cloud computing systems including familiarity with distributed systems for storage and retrieval of data e.g. Hadoop, CASSANDRA, SCALITY, SWIFT, Gluster, Lustre, GPFS, Amazon S3, or another other comparable technology for big data management or High performance computing.
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In-depth knowledge of CS data structures and algorithms. Knowledge of developer tooling across the software development life cycle (task management, source code, building, deployment, test automation and related tools, operations, real-time communication.
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Working with products that support highly distributed, massively parallel computation needs such as Hbase, Big Table, Cassandra, Scality et cetera. One of the following certifications; AWS Certified Solutions Architect, AWS Certified DevOps Engineer, AWS Certified Data Analytics (some flexibility to obtain within four (4) months of starting position.
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Familiarity of NoSQL systems and working with Big Data, such as Elasticsearch, Cassandra, AWS DynamoDB, or other cloud hosted NoSQL databases. Do you know the trade-offs between open source cloud technologies such as Elasticsearch, Cassandra, Kafka, Hadoop, Spark, Flink, Kubernetes, Swarm, etc.
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