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Grid Dynamics

Grid Dynamics is a leading provider of technology consulting, agile co-creation, and scalable engineering and data science services for Fortune 1000 corporations undergoing digital transformation. We work in close collaboration with our clients on digital transformation initiatives that span strategy consulting, early prototypes, and enterprise-scale delivery of new digital platforms. We help organizations become more agile and create innovative digital products and experiences using deep expertise in emerging technology, top global engineering talent, lean software development practices, and a high-performance product culture. Headquartered in Silicon Valley with 4,000 technologists located in engineering delivery centers throughout the US, Mexico, UK, Europe, and India, Grid Dynamics is known for architecting and delivering some of the largest digital transformation programs in the retail, technology, and financial sectors to help its clients win market share, shorten time to market and reduce costs of digital operations on a massive scale.
26/05/2023
Full time
Big Data Engineer (Scala, Spark)
Grid Dynamics Zug, ZG, Switzerland
Full Job Description We are looking for an experienced and technology-proficient Big Data Engineer   to join our team! Our customer is one of the world’s largest technology companies based in Silicon Valley with operations all over the world. In this project, we are working on the bleeding edge of Big Data technology to develop a high-performance data analytics platform, which handles petabytes datasets. Responsibilities: Running big data analytics, and building large scale data infrastructure Detecting meaningful data patterns Assuring the integrity of our data Measuring fraud activity and its impact on campaign and user performance Analyzing the results of mitigations against fraud Requirements: Strong knowledge of Scala In-depth knowledge of Hadoop and Spark, experience with data mining and stream processing technologies (Kafka, Spark Streaming, Akka Streams) Understanding of the best practices in data quality and quality engineering...