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The client Corporation is currently seeking an experienced Big Data Engineer Thesuccessful candidate must have Big Data engineering experience and must demonstrate an affinity forworking with others to create successful solutions.
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Qualifications: Required: Hands-on experience with web development using Java, Spring Boot, HTML5, Java Script, jQuery, CSS. Experience with React/Angular is a big plus. Qualifications 4+ years of full stack application development experience using Java, J2EE, Spring boot, Spring cloud, Spring Data JPA, Spring Integration, ReactJS/Angular, Node.js.
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Proficient in Python/R, PyTorch/TensorFlow, SQL, Big Data and cloud technologies. PhD in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics a big plus.
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Proven experience as a Technical Lead with a focus on Databricks and big data technologies on Azure Cloud Platform. Optimize the performance of big data processing tasks and analytics jobs.
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The D365 Lead DevOps Engineer is responsible for leading the efforts in Microsoft Dynamics 365 Finance & Operations implementations, with a focus on environment management, release pipelines, ADO, automation, and data.
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Strong proficiency in Java Stack programming with expertise in the Spring Boot framework, Apache / Tomcat, Apache Maven, Apache Spark, API Concepts and Methodologies, API Security, Camunda, Cloud Automation, Cloud Computing, Cybersecurity, Data Engineering, Database Design (Physical), DevOps, Java and Related Technology, JavaScript, Linux.
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2+ years experience on Big data pipelines/DAG tools like Airflow, dbt is required. This role will primarily be responsible for building out our analytics platform on Databricks, using dbt and Python for data transformations.
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Responsible for developing and implementing microservices using Java and Spring Boot and writing efficient, and maintainable code to implement business logic, data processing, and integration with other microservices or components.
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Knowledge of some of the main components of Azure Analytics: Azure Data Processing Technologies (DataFactory, Databricks), Azure Big Data Technologies, Databricks, Synapse. Coding experience: Python, C#, Java for Data analysis purpose.
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This role is cloud facing and requires hands on experience in building and managing data engineering code in modern cloud-based technologies such as Google Big Query or equivalent. Prior experience with health information systems and/or patient financial service systems preferred Strong SQL and relational database design/development skills Development experience with cloud-based modern data warehouses such as Google Big Query or equivalent.
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We work with agencies and corporations across the country in the areas of Account Management/Client Services, Brand Strategy, Content and Digital Strategy, Traditional and Digital Media, Marketing Data Analytics, Search Marketing (PPC/SEO) and Social Media.
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Work with big data technologies like Azure Databricks, Apache Spark & Palantir. Five (5) plus years of experience in data engineering, machine learning, and cloud tech developing production grade software components and solutions.
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Human Resources, Telecommuting, Employment Services, Marketing & Sales, Customer Service, IT & Software, Writing & Translation, Data Entry, Remote Work, Online Job Search. WorkAtHome-JobBoard is the leading job search site specializing in the best remote, part-time, freelance, and flexible jobs available.
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In-depth understanding of Google Cloud ecosystem for Data Engineering & MLOps - cloud composer, dataproc, dataproc serverless, big query, cloudrun, vertex, vertex pipelines, GKE. Experience in designing MLOps platforms and architect big data systems on GCP cloud.
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Azure Integration: Work with Azure services such as Azure Data Factory, Azure Databricks, and Azure SQL Database for data integration, processing, and storage. We are seeking a skilled Data Engineer with extensive experience in DBT, Snowflake, and Azure, along with proficiency in Python and SQL. The ideal candidate will have a strong understanding of data modeling principles and a proven track record of developing robust data pipelines and architectures.
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