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Possess Data Warehousing principals and architecture patterns to help build reporting data marts in Oracle, Big Data platforms: Hadoop and Cloud data bases eg: Snowflake/Red Shift Experience and working knowledge in extracting data from traditional data warehouse, big data ecosystems and cloud data warehouses to produce critical data for business analyst executives and operational leaderships.
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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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Managed Azure cloud infrastructure, including Databricks for big data and AI/ML in corporate finance. As a Sr. Lead CloudOps Engineer on Stryker’s Finance Data Science & Engineering team, you are set to lead at the convergence of cloud computing, AI, and financial analytics.
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Some experience with cloud-based and on-prem data solutions (Apache Hadoop, Datastore, Firestore, Cloudera Data Platform, Big Query, Azure SQL, Cosmos DB, Red Shift, Apache Spark, ElastiCache, CloudSQL, Data Bricks, Snowflake, Apache Arrow, Apache Airflow, Flink.
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Proven experience with big data technologies such as Hadoop (YARN, HDFS, MapReduce), Spark (Spark Streaming, Spark SQL), and Kafka. A leading financial institution is seeking a highly skilled Big Data Engineer to join our growing Data Engineering team.
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Experience with big data, machine learning, and numerical programming frameworks (e.g., TensorFlow, Python, MATLAB). Experience in data and information management as it relates to big data trends and issues within businesses.
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Experience building machine learning solutions and leveraging specific machine learning architectures (e.g., deep learning, LSTM, convolutional networks). As a Customer Engineer, you will work with Technical Sales teams as a machine learning subject matter expert to differentiate Google Cloud to our customers.
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Advanced knowledge and experience with big data ecosystems, relational databases, SQL, and business intelligence tools (Tableau or Power BI, etc.) Experience developing analytic techniques such as forecasting, predictive modeling, segmentation analysis, simulation modeling, and data mining.
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Understanding and capabilities with Python, R scripting and other advanced data coding languages. Experience with advanced ETL tools including Tableau Data Prep, PowerQuery, SQL Server Reporting Services and others.
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As a Mr. Cooper Group team member, you play a big role in making that dream possible. Roles & Responsibilities:The Underwriter III is responsible for analyzing highly complex financial and credit information according to internal underwriting standards, investor guidelines, and federal regulationsUnderwrites a wide variety of product and loan types with a higher aggregate credit exposure including underwriting exceptions not determined through DUCompile, analyze, and document loan data and credit risk, borrower and property information.
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All pharmacies carve out a medical plan – your health plan is not paying for those pharmacy claims – CVS Caremark, Optum, and Client are big 3. Right now HM works for finance VP that is where data and reporting and PBM resides.
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Big Data Engineer. A well-known Financial Services Company is looking for a Hadoop Developer with Java in Addison, TX (Hybrid). Big Data Engineer. 4+ years of Software Engineering experience, or equivalent demonstrated through one or a combination of the following: work or consulting experience, training, military experience, education.
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You will play a crucial role in translating business requirements and analytics requests to ensure delivery with a solid understanding of both big data functionality and analytical tool features.
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We were included in the top 10 of Selling Power magazine's "Best Companies to Sell For" list and recognized on Forbes magazine's "Platinum 400 - Best Big Companies" list. Cutting edge sales tools, including a data management device with CRM software.
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Utilizing experience with NLP and text-based extraction techniques;Understanding of how to develop and operationalize data science analytical models so these models can run in an automated context;Understanding of how to map transformation and flow of data from a source to a target system; and,Problem-solving skills and the ability to translate business requirements into data-driven solutions.
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