Position: Lead Big Data Engineer
Company: EPAM Systems
Experience required: 5y
English level: upper

RESPONSIBILITIES
Consulting on migrating the existing HARC MapReduce jobs to AWS EMR
Revisiting source and sinks, data enhancement opportunities (HBase load)
Assistance with migration
Architects understanding dev and prod environments, mapping out how to access production workflows for later operation
Implementation and support of new EMR-based jobs

REQUIREMENTS
Strong hands-on experience as a Big Data Software Engineer with a solid design/development background with Java, Scala, or Python
Experience in delivering data analytics projects and architecture guidelines
Experience in Big Data solutions on-premises and in the cloud (Amazon Web Services, Microsoft Azure, Google Cloud)
Production project experience with Big Data technologies: distributed file systems, Spark, Kafka, Hive, NoSQL (HBase, Cassandra, MongoDB), StreamSet/NiFi, Airflow/Oozie
Knowledge of Agile development methodology, Scrum in particular
Experience in direct customer communications and pre-selling of business-consulting engagements to clients within large enterprise environments
Strong communication skills
Upper-Intermediate or higher English level, both spoken and written (B2+)

WE OFFER
Competitive compensation depending on experience and skills
Individual career path
Unlimited access to LinkedIn learning solutions
Social package - medical insurance, sports
Compensation for sick lists and regular vacations
English classes with native speakers (certified English teachers)
Flexible work hours

ABOUT PROJECT
The client is a global healthcare ISV and one of the top Electronic Medical Record (EMR) vendors. They have a multi-year program to reimagine and re-platform their legacy application suite as a SaaS-based solution using modern technologies and frameworks deployed on AWS cloud.

Search system built with multiple modules and technologies for many years. Its SolR based deployment accounts for very high maintenance and operation costs. A redesign, merging the fragmented modules into a unified platform with low management costs, is planned.