Big Data Engineer with AWS

Desired Skills and Experience:

Experience with architecting, designing and implementing end-to-end cloud infrastructure solutions across leading Cloud platforms with a special focus on Azure and Google Cloud Platform

Proven experience in architecting private cloud/data center solutions using virtualization technologies like VMware, with strong expertise in implementing DevOps/DevSecOps practices and automation strategies

Hands-on experience of leading cloud and infrastructure strategy / current state assessment workshops with client stakeholders and defining target operating model (TOM) / Cloud Center of Excellence driving large-scale cloud migration planning including infrastructure right sizing and cost optimization

Mandatory Skills

• Bachelors degree in computer science, Data Science, engineering, mathematics, information systems, or a related technical discipline

• 3+ years of experience in consulting or client service delivery experience.

• Proven expertise in developing and implementing detection or predictive models for fraud, financial crime, or customer insights.

• Experience with ETL tools like Ab Initio, Informatica, and proficient in data quality management.

• Proficient in at least one or more programming languages: Java, Python, Ruby, Scala

• Hands-on experience with big data technologies such as Hadoop, Spark, and Scala/Java. Familiarity with Python, Elastic, RESTful APIs, JSON, and Tomcat adds an advantage.

• Experience building metrics deck and dashboards for KPIs including the underlying data models.

• Understand how to design, implement, and maintain a platform providing secured access to large datasets

Primary Roles and Responsibilities

An AWS Data Engineer is responsible for designing, building, and maintaining the data infrastructure for an organization using AWS cloud services. This includes creating data pipelines, integrating data from various sources, and implementing data security and privacy measures. The AWS Data Engineer will also be responsible for monitoring and troubleshooting data flows and optimizing data storage and processing for performance and cost efficiency.