Big Data Engineer
- Very strong server-side Java experience, especially in an open source, data-intensive, distributed environments.
- Strong previous professional experience building Distributed Solutions dealing with high volumes of data.
- Hands on experience on HDFS, Hive, Pig, Sqoop and NOSQL.
- Experience/ knowledge working with batch processing/ real-time systems using various open source technologies like Solr, Storm, Kafka, etc.
- Experience in Apache Spark Batch and/or Spark Streaming (at least 6 months)
- Good understanding of algorithms, data structure, performance optimization techniques and exposure to complete SDLC and PDLC
- Well aware of architectural concepts (Multi-tenancy, SOA, SCA etc.) and NFR’s (performance, scalability, monitoring etc.)
- Implementing various solutions arising out of large data processing (GB’s/ PB’s) over various NoSQL, Hadoop and MPP based products—both on-premise and in the cloud
- Actively participating in various architecture and design calls with Big Data customers
- Developing Hive scripts and being involved in writing MapReduce jobs
- Implementing complex projects dealing with considerable data size (GB/PB) and with high complexity
- Leveraging your experience with Hadoop and software engineering to help our customers drive value from their data
- Working with Sr. Architects and providing implementation details to the Offshore team
- Conducting sessions/ writing whitepapers/ Case Studies pertaining to Big Data
- Being responsible for timely and quality deliveries
- Fulfilling organizational responsibilities – sharing knowledge and experience with other passionate Impetus professionals, conducting various technical development sessions and training on new technologies