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Develop and optimize big data processing using Spark and PySpark within the Databricks environment. You should possess strong technical skills in SQL, Python, Spark, and PySpark and have hands-on experience with AWS and Databricks platforms.
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Experience with cloud computing platforms (, AWS, Azure, GCP) and cloud native computingProficiency in programming languages like Python, Java, Scala, C#, SQLExpertise in big data technologies such as Hadoop, Spark, Kafka, and NoSQL databasesKnowledge of data modeling, ETL processes, and data warehousing conceptsExceptional problem-solving abilities and teamwork skills.
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Strong foundational understanding of Big Data ecosystems to make trade-off decisions while developing end to end solutions on Cloud/On-Prem/Hybrid Data tech stackExperience balancing new product development with maintaining day-to-day data reporting operations in a very large scale enterprise set upExperience applying both qualitative analysis and quantitative analysis techniques and familiarity with common data science models and approaches to problem-solving.
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Hands on experience with programming for extract transform operations and data analysis using languages such as, but not limited to R, Python, SQL, and Excel VBA. Experience in developing data visualization and business intelligence solutions using tools such as, but not limited to, Shiny, Tableau, PowerBI, and front-end technologies and languages.
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Develop your professional skills through one-on-one mentoring from a Data Science Lead assigned to you over the summer. Current PhD Student in Quantitative Discipline related to Data Science, including but not limited to Statistics, Mathematics, Economics, Computer Science, etc.
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Requirements: Requires a Master’s in Statistics, Computer Science, Data Science, Machine Learning, Applied Math, Operations Research, Economics, or a related field plus two (2) years of experience as a Data Scientist, Data Engineer, or other occupation/position/job title involving research and data analysis.
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Hands on experience in data engineering desired with extensive knowledge of SQL and PySpark, on Databricks Platform. Ability to design cloud and on premise data solutions and provide guidance across storage, big data platform services, Big Data ecosystem, vendor SaaS integrations, RDBMS and NoSQL databases is a plus.
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Hands-on experience with dbt for data modeling and transformations. 5+ years of experience in data engineering, with a strong focus on AWS, Redshift, and Snowflake.
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Should have worked on big data initiatives & scalable data architecture like Hadoop, Hive, Cloudera, Spark. Hands on experience in dealing with structured and unstructured data from multiple sources and applying machine learning techniques and algorithms to discover patterns, intents and build knowledge repositories and graphs to solve enterprise problems.
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2+ years of experience in a data engineering/science role OR a degree in data science or a similar discipline. Experience with financial datasets (e.g. Refinitiv, S&P, Bloomberg) is a big plus.
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Troubleshooting and debugging data-related issues on the AWS Databricks platform. Integrating Databricks with other AWS products such as SNS, SQS, and MSK.What we are looking for: Bachelor's or master's degree in computer science, Information Technology, or a related field.
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Data Integration/ETL Lead with hands-on experience in designing and implementing data warehouses and data marts for large financial institutions. Function as data and integration SME and supply business and technical knowledge on required subject-areas and systems.
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Minimum 5 years’ experience in data engineering with a proven track record in the Azure ecosystem and with Azure DevOps. Minimum 5 years’ experience with Data Modeling, ETL, and Data WarehousingMinimum 3 years’ hands on experience with Azure Services.
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Preferred Skills:- Knowledge of Microsoft Office Suite including Outlook, Word, Excel, PowerPoint and SharePoint- Ability to train on database systems such as the Agency’s Laboratory Information Management System (LIMS) and Water Information Systems by Kisters (WISKI) data management systems- A general knowledge of field sampling equipment and standard water quality sensors.
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New York Institute of Technology's six schools and colleges offer undergraduate, graduate, and professional degree programs in in-demand disciplines including computer science, data science, and cybersecurity; biology, health professions, and medicine; architecture and design; engineering; IT and digital technologies; management; and energy and sustainability.
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data science big hands on jobs Title: scientist Company: Agoda in New York, NY
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