Lead/Sr Data Cloud Engineer (Critical, High Priority)
Please DO NOT submit non-locals here. Need local candidates who are ok to be onsite all 5 days
UST Global is looking for a Sr Data Cloud Engineer to work with one of the leading healthcare providers in US. The ideal candidate may possess good background on Healthcare Business
Responsibilities
Lead data engineer and analyst to deliver data sets and analysis results as per business requirements.
Assemble large, complex data sets that meets functional/Non-Functional business requirements.
Automating manual processes, optimizing data delivery, recommending platform greater scalability/improvements
Collaborate with initiative leads to optimize and enhance new capabilities.
Mentor team in migrating Hadoop on-prem to cloud AWS and Glue
Create and maintain optimal data pipeline architecture.
Presenting analysis results/recommendations using Powerpoint
Requirements
Mandatory skills:
Hands on experience in migrating Hadoop on-prem to cloud platform, AWS, S3, Glue
Experience in analyzing data using Big-Data platform Spark, Scala, Hive
Experience in analyzing data using AWS Cloud, Glue, Python, Pyspark
Strong Analytical skills in relating multiple data sets and identify patterns
Hands on experience in writing advanced SQL queries, familiarity with variety of database
Experience in building and optimizing Big-Data pipelines, architecture, and data sets
Experience in Hadoop file formats like ORC, Avro, Parquet, CSV
Experience in NoSQL databases like MongoDB/document DB
:
9 yrs IT experience and good expertise in SDLC/Agile
5 yrs in Scala, Spark, Hive
5 yrs in programing language (Python, Bigdata)
3 yrs in AWS, S3, Glue colud platform
Hands on experience in Big-Data, AWS, Python, Spark, Scala, MongoDB
Strong skills in writing complex SQL queries.
Hands on experience in migrating Hadoop on-prem to cloud AWS/Glue
Build and optimize Big-Data pipelines, architecture, and data sets
Implement Python flex APIs to share data insights with digital systems
Desired/Preferred:
Scheduling tools like Control-M, Oozie
NoSQL databases like MongoDB/document DB
Implement Python flex APIs to share data insights to digital systems