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Our client is the largest credit card processor in the world. It invented electronic payment-clearing credit card transactions. It processes 65000 credit card transactions every second of every day. It is a digital product company with very large data and analytics functions with large product and engineering teams behind them. As the world’s leader in digital payments technology, its mission is to connect the world through the most creative, reliable and secure payment network - enabling individuals, businesses, and economies to thrive.

We are looking for an experienced Big Data Engineer specializing in Scala, Hadoop, and Spark within a large enterprise digital product company. The role involves a mix of technical proficiency, strategic thinking, collaboration, and proactive problem-solving to deliver robust and scalable data solutions.

Essential functions

1. Design and Develop Big Data Solutions : Architect, design, and implement scalable and reliable big data solutions using Scala, Hadoop, and Spark technologies to meet business requirements.
2. Data Ingestion and Processing : Develop robust data ingestion processes to acquire, clean, and transform large volumes of structured and unstructured data from various sources into usable formats.
3. Optimize Data Pipelines : Build and optimize data pipelines and workflows for efficient data processing, storage, and retrieval, ensuring high performance and low latency.
4. Cluster Management and Optimization : Manage and optimize Hadoop cluster resources to ensure high availability, reliability, and scalability of big data applications.
5. Real-Time Data Processing : Implement real-time data processing solutions using Spark Streaming or other similar technologies to enable real-time analytics and decision-making.
6. Data Quality and Governance : Establish and enforce data quality standards, data governance policies, and best practices to ensure data accuracy, consistency, and integrity across systems.
7. Performance Tuning and Monitoring : Monitor system performance, troubleshoot bottlenecks, and conduct performance tuning of Spark jobs and Hadoop clusters to optimize resource utilization and job execution times.

Qualifications

1. Strong Programming Skills : Proficiency in Scala and Java, including a deep understanding of functional programming concepts in Scala.
2. Expertise in Big Data Technologies : Extensive experience with Apache Hadoop ecosystem components such as HDFS, MapReduce, Hive, Pig, and HBase.
3. Advanced Spark Knowledge : In-depth knowledge of Apache Spark, including Spark Core, Spark SQL, Spark Streaming, and MLlib for large-scale data processing and analytics.
4. Data Modeling and ETL : Experience with data modeling, schema design, and implementing efficient ETL (Extract, Transform, Load) processes using big data technologies.
5. Cluster Management Tools : Hands-on experience with cluster management tools like Apache YARN, Apache Mesos, or Kubernetes for resource allocation and job scheduling.
6. SQL and NoSQL Databases : Proficiency in SQL for querying and analyzing data stored in relational databases (e.g., MySQL, PostgreSQL) and NoSQL databases (e.g., MongoDB, Cassandra).

We offer

1. Opportunity to work on bleeding-edge projects
2. Work with a highly motivated and dedicated team
3. Benefits package - medical insurance, sports
4. Corporate social events
5. Well-equipped office

About us

Grid Dynamics (Nasdaq:GDYN) is a digital-native technology services provider that accelerates growth and bolsters competitive advantage for Fortune 1000 companies. Grid Dynamics provides digital transformation consulting and implementation services in omnichannel customer experience, big data analytics, search, artificial intelligence, cloud migration, and application modernization. Grid Dynamics achieves high speed-to-market, quality, and efficiency by using technology accelerators, an agile delivery culture, and its pool of global engineering talent. Founded in 2006, Grid Dynamics is headquartered in Silicon Valley with offices across the US, UK, Netherlands, Mexico, and Central and Eastern Europe. To learn more about Grid Dynamics, please visit Follow us on Facebook, Twitter, and LinkedIn.

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