Required Skill set: 

AWS DMS, S3, GLUE, KINESIS, LAMBDA, ATHENA, EMR, SAGE MAKER, Redshift, RDS, Metadata Management, Data Governance, Security, Big Data, Data Quality and Recovery

1. Work directly with executives of IT and Business and principal engineers to orient the data team s strategy with the company goals
2. Lead data engineering team to design and implement data stores, pipelines, ETL routines and API access
3. Lead data product teams to consume data and make it available to both internal and external customers for analysis, troubleshooting, BI, predictive use cases, etc.
4. Designing and implementing Data Warehouse for large-scale high-volume data loads for customer action and churn analysis
5. Design and implement features built on machine learning, such as customer behavior, churn, next best action, next best offer, etc.

Experience & Skill Requirements:

 Expert level solution architecture skills in the following:

1. Metadata Management
2. Data Governance
3. Data Security
4. Big Data
5. Data Quality and Recovery

Expert level skill with hands-on experience in the following:

1. Migrate on-premise database to AWS S3 with AWS Database Migration Service (DMS)
2. Setup AWS Glue to prepare data for analysis through automated extract, transform and load (ETL) processes
3. Setup AWS Kinesis to process hundreds of terabytes per hour from high volumes of streaming data from various sources
4. Develop event driven data processing pipeline code and execute on AWS Lambda
5. Develop interactive query with AWS Athena to analyze the data in AWS S3
6. Setup AWS Elastic MapReduce (EMR) tool EC2 instances with Hadoop (and optionally Hive and/or Pig) installed and configured on them, to be able to process big data and perform analysis
7. Build and train machine learning models for predictive or analytical applications in the AWS SageMaker, with experience in creating notebook instance, prepare data, train the model from the data, deploy and evaluate the model performance
8. Setup data warehouse with Amazon Redshift. Experience in creating Redshift clusters, upload data sets and perform data analysis queries.

Eligibility Requirements:

• 3+ years of experience working with AWS DMS, S3, GLUE, KINESIS, LAMBDA, ATHENA, EMR, SAGE MAKER, Redshift, RDS
• 5+ years on Big Data (Hadoop, Spark, Java, Scala, Python)
• 5+ years of full-time Python development experience
• 3+ years of Redshift experience. Understanding of relational data models is a must
• 3+ years of experience working with big data architectures in high-volume environments
• Extensive experience building and managing ETL pipelines on cloud-based platforms from inception to production rollout