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Proficiency in Databricks, Azure services (e.g., Azure Data Lake Storage, Azure Synapse Analytics), and Spark for big data processing and analytics. Stay up-to-date with emerging technologies and best practices in data engineering, cloud computing, and big data analytics.
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Softrams is a Maryland and Virginia-based small business information technology, consulting, and solutions provider specializing in emerging technologies for UX/UI, mobile apps, DevOps, big data analytics, data science, and cyber security.
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Candidate will be given the opportunity to learn and make use of sophisticated tools (SQL, Alteryx, Informatica, SAS, Tableau, SSRS, DataRobot etc) and technologies (Big data like Hadoop, Python/R Programming, AI/Data Science,) to provide customized automated solutions across multiple business segments.
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Solid experience with architecting and implementing metadata management including data catalogues, data lineage, data quality and data observability for big data workflows.
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Certified Analytics Professional (CAP), Data Science Council of America (DASCA), Principal Data Scientist (PDS), SAS Big Data Professional, Microsoft Certified Azure Data Scientist Associate, or equivalents.
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Solid experience implementing data lineage, data quality and data observability for big data workflows. Proficiency in data engineering tools and technologies - SQL, Python, Spark, DBT, Airflow, Kafka.
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Mercer Digital specializes in business and cloud consulting, with a goal of transforming our clients' businesses by leveraging expertise in big data, cloud computing, and enterprise services.
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Derek Bang, the practice founder and inventor of the group’s flagship software product for net revenue reporting, Revenue Cycle Analytics (RCA) came to Crowe nearly 20 years ago with an idea for a strategic pursuit in the Healthcare Industry through his previous experience as a Healthcare Auditor at a Big 4 firm.
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Experience deploying and maintaining pipelines (AWS, Docker, Airflow) and in engineering big-data solutions using technologies like Databricks, S3, and Spark. Feature Engineering and Optimization: Develop and maintain ETL pipelines using orchestration tools such as Airflow and Jenkins; deploy scalable streaming and batch data pipelines to support petabyte scale datasets.
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Our transformative solutions are powered by artificial intelligence, machine learning, facial recognition, cloud solutions and Big Data analytics. Build and configure demo scenarios in Genesys or NICE CXone Cloud Admin and Architect including ACD, IVR, outbound dialers, queuing, skilling, quality, ACD, Email, Web Messaging, and SMS and other digital channels.
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Solid fundamentals with HTML/CSS.Web development experience, especially with JavaScript (Node.js, Vue.js, React), or Python (Flask)Experience with big data techniquesExposure to Agile software development methodology.
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The Inform and Disseminate Scientific, Analytics and Visualization Team provides high-level public health expertise on all phases of statistical models and interpretation of analysis and studies on big data sets of surveillance and purchased clinical data for performing data analysis.
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Experience with big data technology (Spark) and experience with cloud platforms (AWS, Google Cloud) is desirable. We are looking for a Data Scientist who will collaborate with a multidisciplinary team to support our Prioritization and Triage engine.
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Building data ingestion pipelines is highly technical and requires advanced skills with computers and proficiency with Big Data tools and technologies such as SQL, Python/Scala, Spark, Spark Structured Streaming, Spark SQL, Kafka, Sqoop, Hive, Kudu, HBASE, Impala, S3, HDFS, or Cloud platforms such as AWS, GCP, Azure, etc.
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Insightful experience with big data technologies like Hadoop, real-time processing frameworks (e.g., Apache Spark), and advanced knowledge of SQL and data architecture. For example, reconciliation using Big Data technologies, growing and optimizing customer payments options, tax calculations in milli-second response times and a mesh of supplier payment options like virtual credit cards.
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