Big Data Developer @ Westerville, OH(Need min 9+yrs)

March 06, 2019

Big Data Developer @ Westerville, OH(Need min 9+yrs)

Big Data Developer

Location: Westerville, OH

Duration: Long Term

Required Skills:

Education: Bachelor's degree in Engineering - Computer Science, or Information Technology. Master's degree in Finance, Computer Science, or Information Technology a plus

Qualifications

9+ years of total IT experience including 4+ years of Big Data experience
Person need to be very strong in Spark Streaming and Kafka (first preference). Otherwise, the second preference is strong in Spark SQL (Scala or Python) and Hive.
Experience on Cloudera distribution and Cloudera Data Hub (CDH) is a plus
Proficient in Linux/Unix scripting
Knowledge on PeopleSoft data structures and HR related data is a plus
Experience in Agile methodology is a must
Knowledge of standard methodologies, concepts, best practices, and procedures within Big Data environment
Exposure to infrastructure as service (IAAS) providers such as: Google Compute Engine, Microsoft Azure or Amazon AWS is a plus
Self-starter and able to independently implement the solution
Good problem-solving techniques and communication

Job Description

Hands on Big Data developer role
Develop data pipelines using Big Data technologies that leverage value to the customer; understand customer use cases and workflows and translate them into engineering deliverables
Actively participate in scrum calls, story points, estimates and own the development piece
Analyze the user stories, understand the requirements and develop the code as per the design
Develop test cases, perform unit testing and integrating testing
Support QA Testing, UAT and production deployment
Develop batch and real-time data load jobs from a broad variety of data sources into Hadoop
Design ETL jobs to read data from Hadoop and pass to variety of consumers / downstream applications
Perform analysis of vast data stores and uncover insights
Analyze the long running queries and jobs, performance tune them by using query optimization techniques and Spark code optimization