Big Data Architect (Open source contributor)

HCLTech is a global technology company, home to 219,000+ people across 54 countries, delivering industry-leading capabilities centered on digital, engineering and cloud, powered by a broad portfolio of technology services and products. We work with clients across all major verticals, providing industry solutions for Financial Services, Manufacturing, Life Sciences and Healthcare, Technology and Services, Telecom and Media, Retail and CPG, and Public Services. Consolidated revenues as of $13+ billion.

Location – London

Skill – Apache Hadoop

We are looking for open-source contributors to Apache projects, who have an in-depth understanding of the code behind the Apache ecosystem, should have experience in Cloudera or similar distribution and should possess in depth knowledge of bigdata tech stack.

Requirement:

Experience of platform engineering along with application engineering (hands-on)

Experience in design of an open source platform based on Apache framework for Hadoop.

Experience in integrating Infra-as-a-Code in their platform (Bespoke implementation from scratch)

Experience of design & architect work for the open source Apache platform in hybrid cloud environment

Ability to do debug & fix code in the open source Apache code and should be an individual contributor to open source projects.

Job description: The Apache Hadoop project requires up to 3 individuals with experience in designing and building platforms, and supporting applications both in cloud environments and on-premises.

These resources are expected to be open source contributors to Apache projects, have an in-depth understanding of the code behind the Apache ecosystem, and be capable of identifying and fixing complex issues during delivery.

3 individuals required to support all developers in migrating and debugging various RiskFinder critical applications.

They need to be \”Developers\” who are expert in designing and building Big Data platforms using Apache Hadoop and support Apache Hadoop implementations both in cloud environments and on-premises.