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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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Oversee the transformation and refactoring of modules into scalable, efficient microservices using Databricks. Expertise in using Databricks for designing and managing scalable microservices.
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Industry : Oil & Gas Applications/ Tools : Azure, Cloud, Azure Functions, Azure SQL, Azure DevOps, GitHub, Microservices and App services, Microservices and App services, Cloud Managed SQL, Big data/data lakes, AI and Machine Learning, Azure Blobs, Office 365.
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Experience with data management technologies such as Databricks, Apache Spark, Hadoop, Kafka. Experience with data science tools such as Python, R, PyTorch, TensorFlow, Kera's, NLTK, Spacy, or Neo4j, and a good understanding of modeling platforms such as Sage Maker, Databricks, and Dataiku.
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Proficiency with Python, Terraform, Microservices. Expertise in contemporary design paradigms, microservices, and cloud-native design. Strong AWS and Databricks experience preferred, or Snowflake.
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A constant desire to improve, learn more and take things to the next level and a passion for adopting the latest features in Java, Spring Boot, Java, Spring Boot, Azure SQL, Kubernetes, Apache Kafka, and Google Cloud Platform.
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5+ years of big data design experience with technical stacks like Spark, Flink, Druid, Clickhouse, Single Store, Snowflake, Kafka, Nifi and AWS big data technologies. Proven track record with cloud infrastructure technologies, at least two of Terraform, K8S, Spinnaker, IAM, ALB, and etc.
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Big data distributed programming languages, and ecosystems: Spark, Hadoop, MapReduce, Pig, Kafka. common data science tools such as Python, R, Pytorch, TensorFlow, Keras, NLTK, Spacy, or Neo4j, and a good understanding of modelling platforms (Azure AutoML, SageMaker, DataBricks, DataRobot and H2O.ai.
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Write data ingestion and processing Python-based programs for application backend cloud microservices. Develop processing pipelines for remote sensing and geospatial data streams in Google Cloud.
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Expertise in building REST-based microservices in a serverless architecture. 5 years’ experience implementing AWS technologies (Lambda, Step Functions, CloudWatch, CloudFront, Cognito, route53, Dynamo DB, ECS, EC2, S3, RDS, API Gateway.
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Experience with modern software development principles including code management, test automation, APIs, microservices, and cloud services. All applicants must be U.S. persons within the meaning of ITAR. ITAR defines a U.S. person as a U.S. Citizen, U.S. Permanent Resident (i.e. 'Green Card Holder'), Political Asylee, or Refugee.
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Knowledgeable in Internet/Web technologies, ITIL best practices, network technologies, AWS logging (CloudTrail, CloudWatch, VPC Flow Logs), SNMP monitoring/reporting, CI/CD tools (Artifactory, Jenkins, GIT), cloud-native applications with Terraform, microservices support technologies, encryption (SSL/TLS, PKI), and software technology security controls.
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Along with this they will leverage software architecture and design patterns to develop fault tolerant microservices. Experience in developing API endpoints and microservices. The Python Software Engineer will work closely with Data Engineers and Data Scientists to solve real-world oil and gas midstream problems using advanced analytics and machine learning.
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Spring boot, Microservices, Docker, Jenkins and REST API's experience. Preferred skills: NLP, Text mining, Tableau, PowerBI, SAS, Tensorflow. Spring boot, Microservices, Docker, Jenkins and REST API's experience.
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Experience in Object-Oriented Design, MVC, MVVM patterns, Test-Driven Development, microservices architecture, and Restful APIs. NodeJS, JavaScript, React, Angular, Python. We’re in need of a fullstack engineer for one of our clients who is a prominent leader in testing services for the environmental and energy markets.
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