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Solutions Lead

ENGLEWOOD-80155, CO, US
12/30/2020

-

Required Skills

Apache Kafka, Spark programming language

Company

E-Solutions

Experience

Job Description

Check for handling Real-time High volume, high input data ingestion in the capacity of Architect validate against the Use Case they have implemented
To validate the relevance on Tech Stack - check what is the
Streaming platform used Kenis or Kafka [either 1 should be ok]
Processing Framework usage check for Spark or any relevant ones
Target data store used for Data Ingestion
Any Cloud experience

Job Duties and Responsibilities
Primary responsibilities fall into the following categories:
Deploy enterprise-ready, secure and compliant data-oriented solutions leveraging Data Warehouse, Big Data and Machine Learning frameworks
Optimizing data engineering and machine learning pipelines
Reviews architectural designs to ensure consistency & alignment with defined target architecture and adherence to established architecture standards
Support data and cloud transformation initiatives
Contribute to our cloud strategy based on prior experience
Understand the latest technologies in a rapidly innovative marketplace
Independently work with all stakeholders across the organization to deliver point and strategic solutions
Assist solution providers with the definition and implementation of technical and business strategies
Skills - Experience and Requirements
A successful Solution Lead will have the following:
Should have prior experience in working as a Data warehouse/Big Data architect.
Experience in advanced Apache Spark processing framework, spark programming languages such as Scala/Python/Advanced Java with sound knowledge in shell scripting.
Should have experience in both functional programming and Spark SQL programming dealing with processing terabytes of data
Specifically, this experience must be in writing Big Data data engineering jobs for large scale data integration in AWS. Prior experience in writing Machine Learning data pipelines using Spark programming language is an added advantage.
Advanced SQL experience including SQL performance tuning is a must.
Should have worked on other big data frameworks such as MapReduce, HDFS, Hive/Impala, AWS Athena.
Experience in logical & physical table design in Big Data environment to suite processing frameworks
Knowledge of using, setting up and tuning resource management framework such as Yarn, Mesos or standalone spark.
Experience in writing spark streaming jobs (producers/consumers) using Apache Kafka or AWS Kinesis is required
Should have knowledge in variety of data platforms such as Redshift, S3, Teradata, Hbase, MySQL/Postgres, MongoDB
Experience in AWS services such as EMR, Glue, S3, Athena, DynamoDB, IAM, Lambda, Cloud watch and Data pipeline
Must have used the technologies for deploying specific solutions in the area of Big Data and Machine learning.
Experience in AWS cloud transformation projects are required.
Telecommunication experience is an added advantage