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Lead Big Data Developer

3 months ago

Rockville, United States B. I. R. Solutions, LLC Full time

Apply only if you possess valid / Genuine work authorization.

Position:  Lead Big Data Developer

Location: Rockville, MD (onsite once a Month)

Length: Long Term Contract

Term : Contract (C2C)

 

Please apply if you have completed at least three projects as a Lead Big Data Developer real-time experience. Background and employment verification will be conducted.

Interview Process: Candidates who pass the written test will proceed to three rounds of technical interviews conducted via video call, which will include live coding scenarios.

If you are interested, please send your resume to hr@birsolutions.com.

Job Description: 

 

B.I.R Solutions is seeking a highly motivated Lead Big Data Developer to support our client. The ideal candidate need to  meet the following criteria: 

  

Requirements: 

· 10+ years of development experience in Java AND Scala working on ETL projects in an Agile environment

· 10+ years of experience in big data projects

· 10+ years of experience with API development (REST API’s) and processing with JSON and csv files

· 10+ Years of experience working in a Lead role supporting design of applications and ETL projects

· Strong knowledge of big data technologies including: AWS EMR, AWS EKS, Apache Spark, SQL & serverless technologies like AWS Dynamo DB, AWS Lambda

· Unit testing using JUnit or ScalaTest

 

Desired: 

· Business Process Management experience using AWS functions

· Python & Shell Scripting

· Experience with performance testing tools such as Gatling or Jmeter

 

Responsibilities:

 · Comprehend Complex Business Requirements

· Design and develop ETL pipelines to collect, validate, and transform data according to the requirement

· Develop automated unit tests, functional tests and performance tests.

· Support optimal data pipeline architecture

· Design ETL jobs for seamless execution in AWS cloud environment

· Reduce processing time and cost of ETL workloads

· Lead cross-functional review meetings with peers and stakeholders

· Continuous data quality checks.

· Identify areas where machine learning can be used to identify data anomalies