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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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2-3 years’ experience as an Investment Banking analyst, Big Four consultant, Big Four auditor, or Fortune 100 FP&A analyst. Data Center/ REIT/ Technology / SaaS business experience is a plus, but not required.
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You have 5 or more years of experience using Python, SQL, Databricks, Spark, with Big Data. You will work with software engineers and data scientists to integrate ML models into production systems on the cloud and in embedded systems.
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Demonstrated understanding and experience using software and tools including big data tools like Kafka, Spark; relational SQL databases including Big Query or equivalent; workflow management and pipeline tools such as Airflow, and object function/object-oriented scripting languages including Scala, C.
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Basic idea of Big Data on GCP BigQuery, Dataproc, Data lake and Dataflow services. Proficiency in Google security and compliance tools like IAM, KMS, Resource Manager etc. Basic idea of Big Data on GCP BigQuery, Dataproc, Data lake and Dataflow services.
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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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Strong knowledge and experience with big data platforms (AWS, Azure, Spark) and ML/AI languages and tools (Python, TensorFlow, PyTorch) Graduate degree in computer science, data science, engineering, physics, mathematics, statistics, or other quantitative fields.
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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.
$142,000 - $214,000 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Provide architectural assessments and work-effort estimates and keep abreast of Cloud trends and standards, big data technologies, master data management, self-service business intelligence, metadata management, data quality, data services, and data governance.
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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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Experience with Tableau, or other data visualization software is a also plus. We work with big, exciting beverage brands and the #1 single-serve coffee brewing system in North America at KDP, and we have fun doing it.
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