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Visualization Tools: Tableau (preferred), QlikView, Power BI Data Quality: Understanding of data quality best practices and experience in implementing data governance principles. Qualifications: Bachelor’s or Master’s degree in Analytics/ Data science (or) any Math/Statistics/Quant background, or Bachelor’s degree in Information Technology or Computer Science with proven experience in data engineering/ business intelligence tools especially strong understanding of database concepts and data modeling.
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Led by the Chief Information Security Officer (CISO), Technology Risk secures Goldman Sachs against hackers and other cyber threats. The Sensor Grid engineering team is responsible for all security sensors within Technology Risk. You will work closely with Threat Management Centre, Hunt, Detection Engineering, and Data Science teams to ensure our sensors and data streams are effectively configured, streamlined, integrated, automated and monitored to ensure the systems and data necessary to protect the firm from cyber threats is available.
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Vice President, Software Engineering w/ Goldman Sachs Services LLC in Dallas, Texas. Goldman Sachs is an equal employment/affirmative action employer Female/Minority/Disability/Veteran/Sexual Orientation/Gender Identity.
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Vice President, Software Engineering with Goldman Sachs & Co. ©The Goldman Sachs Group, Inc., 2024. or Java enterprise applications within a Unix environment; programming technologies including Java, Spring, Hibernate, RabbitMQ, or Kafka; relational database management system (RDBMS) such as Sybase or Oracle; micro-services architecture design and development including REST, Java, Spring, or other back-end technologies; and database query languages, including SQL or NoSQL technologies such as MongoDB.
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Connect events (e.g., macroeconomic data releases, political elections) to potential vulnerabilities. At Goldman Sachs, we commit our people, capital and ideas to help our clients, shareholders and the communities we serve to grow.
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Or similar; full software development lifecycle, including requirements gathering, design, coding, testing, documentation, deployment, and production support; microservices architecture, REST APIs, and data analysis on large datasets using SQL, Python, or PySpark; and Sybase and other relational database management systems (RDBMS) as well as database query language.
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