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About Near :
Near is the world's largest source of intelligence on people and places, processing data from over 1.6 billion monthly users across 44 countries. The Near Platform powers data-driven marketing and enrichment offerings through a suite of SaaS products. The users of the platform can leverage audience, spatial, retail, among other data in a privacy-led environment.
Founded in 2012, Near is headquartered in Singapore with offices in San Francisco, New York, London, Bangalore, Tokyo and Sydney. Today, marquee brands such as News Corp, MetLife, The We Company and Mastercard work with Near to provide enhanced customer experiences.
Near is backed by leading investors including Sequoia Capital, JP Morgan Private Equity Group, Cisco Investments and Telstra Ventures. Visit www.near.co to find out more.
Tasks include :
- Designing and implementing our data processing pipelines for different kinds of data sources, formats and content for the Near Platform.
- Developing techniques to analyse and enhance both structured and unstructured data.
- Develop/operationalize data science models in application contexts.
- Participate in product design and development activities supporting Near's suite of location intelligence products.
Skills :
- Must have worked with distributed data processing frameworks - Apache Spark, Apache Flink or Hadoop.
- In-depth understanding & experience in big data technologies (Kafka, Cassandra, HBase, Spark).
- Knowledge of frameworks & distributed systems, good at algorithms, data structures, core java, design pattern.
- Understanding of distributed system fundamentals.
- Must have built data processing framework.
- Preferably to have worked with microservices.
Requirements :
- We are looking for an engineer with Bachelor- s/Master's degree.
- Overall 6-9 years of experience with at least minimum 4 years working experience on any Data driven company/platform.
- Exceptional problem solving, analytical and organisation skills with a detail-oriented attitude.
- Passion for learning new technologies.
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