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Proficient in big data frameworks such as Flink (preferred), Hadoop, Beam, Spark, etc. Hands-on implementation of streaming data processing engine. Set technical direction for streaming data processing engine.
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The Lead Data Engineer is responsible for orchestrating, deploying, maintaining and scaling cloud infrastructure targeting big data and platform data management (e.g., data warehouses, data lakes) including data access APIs. Prepares and manipulates data using Hadoop or equivalent.
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Expertise in Databricks, Apache Spark, and big data technologies. Architect and implement big data processing solutions leveraging Apache Spark for ETL, data transformation, and machine learning workflows.
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Job Title: AI Big Data Engineer. Experience with big data frameworks and tools, such as Spark, Hadoop, Kafka and Hive. Experience building production systems with more modern ETL, ELT and data systems, such as AWS Glue, Databricks, Snowflake, Elastic, and Azure Cognitive Search.
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Specific experience in writing Big Data engineering for large-scale data integration in AWS; prior experience in writing Machine Learning data pipelines using Spark programming language is an added advantage.
$181,500 a yearFull-timeExpandApply NowActive JobUpdated 3 days ago - UpvoteDownvoteShare Job
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Proven experience on Hadoop/Big Data migration to Snowflake: Having Spark and Spark SQL experience is critical to know what Snowflake connectors used in the conversion. We are looking to fill a Long-term Contract role as Big Data Engineer in Memphis, TN.
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Cloud & Big Data Integration: Lead the migration and integration of data systems to cloud environments (AWS, Azure, GCP) and implement big data solutions as needed for advanced analytics and reporting.
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3+ years of experience developing big data technologies with Spark and Hive, preferably leveraging such as DataBricks, Juypter notebooks, or GCP, AWS, and Azure equivalent technology. Design, implement, and deploy data applications and mechanisms using big data technology.
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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. Utilize ETL tools and frameworks (e.g., Apache Airflow, Talend) to automate data workflows, ensuring efficient data integration and timely availability of data for analytics.
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Experience with Big Data frameworks (e.g. Hadoop MapReduce, Spark, Hive, Kafka) Expertise with Google Cloud Platform’s distributed Machine Learning, Data Science and Data Engineering tools (e.g. BigQuery ML, VertexAI, AutoML, Docker, Kubernetes, Kubeflow, Dataproc.
$140,000 a yearFull-timeExpandApply NowActive JobUpdated 6 days ago - UpvoteDownvoteShare Job
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Apache Hadoop or Apache Spark (for big data processing) Data Warehousing (e.g., AWS Redshift, Google BigQuery) Natural Language Processing (NLP) Tools and Frameworks (e.g., Hugging Face, AWS Comprehend Medical for extracting insights from clinical text data.
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Docker, Kubernetes, ECS, EKS or AWS Fargate (for containerization and orchestration of data applications and reproducibility) Data Visualization Tools (e.g., Tableau, Power BI, Plotly) The Data Scientist will be responsible for driving insights from the vast amounts of patient and environmental data available within our data warehouse.
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The candidate should have expertise in data modeling, SQL, Python, R, data warehousing, big data technologies, data pipeline construction, data quality, cleansing, version control, cloud services, machine learning, feature engineering, deep learning, and natural language processing.
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HIPAA Compliance (handling sensitive patient data securely) Data Privacy (understanding of privacy laws such as HIPAA, GDPR) Work closely with researcher teams to design analysis specifications, including input data specifications, data cleaning, algorithms, and interpretation of results.
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Data Scientist (Healthcare) 100% Remote. Data Science & Machine Learning Frameworks. Python (for preprocessing, data analysis, machine learning, scripting) Data Governance & Security.
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big data jobs Title: career in MO, Us
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Building a candidate pipeline through a great internship program for local college students and recent graduates at local universities is a great and cost-effective way to attract and retain top talent. By offering meaningful and impactful work experiences, regular feedback, coaching, and mentorship, you can create a positive internship experience that will make your organization a sought-after destination for future employees. This not only benefits the organization in the short-term but also in the long-term, as you'll have a pool of well-trained and experienced candidates who may be interested in full-time employment once they graduate. Furthermore, building relationships with local universities and college students can increase brand awareness and build a positive reputation for your organization in the local community.