Data Architect

A leading venture capitalist (VC) in Silicon Valley commented that “Evergent is a diamond in the rough”. Evergent today manages over 560M+ user accounts in over 180+ countries on behalf of our customers. Globally Evergent is working with 5 of the top 10 carriers (AT&T, Etisalat, SingTel, Telkomsel, and AirTel) and 4 of the top 10 media companies (HBO, FOX, SONY  and BBC). We are not surprised by the VC comment. We have done this with an amazing global team of 300+ professionals. Evergent is recognized as the global leader for Customer Lifecycle Management for launching new revenue streams without disturbing the inflexible legacy systems.  The need for digital transformation in this subscription economy and our ability to launch services in weeks is what sets Evergent apart. We welcome you to come and meet with us.

Evergent is seeking an Artificial Intelligence/Machine Learning Data Architect with experience collecting, analyzing, and interpreting large data sets in order to develop data-driven solutions.

The DA provides expert support, analysis and research into large data problems and processes. Applies advanced technical principles, theories, and concepts. Contributes to the development of new principles and concepts. Works on data-centered technical problems and provides solutions which are highly innovative. Determines and pursues courses of action necessary to obtain desired results.

Required Qualifications:

  • Bachelor's Degree and 10 years of related experience in Information Technology or the equivalent combination of education, professional training, or work experience in lieu of a degree.

  • 5 years of experience in working with data AI/ML.

  • Expertise in Data Management best practices, design and engineering for large-scale data systems.

  • Ability to translate end-user’s high-level requirements into detailed analytics to be processed in SIEM, AI/ML custom and cloud-managed solutions.

  • Ability to assist in the architecture and development of a DevSecOps, CI/CD analytics development pipeline environment using in-house and Cloud-based technologies.

  • Architecting and engineering modern, federated / disparate data stores into a unified data platform including the ability to create/expose data oriented APIs.

  • Centralized and distributed database design and engineering that supports flexible indexing and federation of query capabilities.

  • Data governance design and meta-data and data tag management with robust data encryption and data sharing capabilities.

  • Monitoring performance and capacity and advising on any necessary infrastructure changes.

  • Defining data retention policies.

  • Knowledge and use of Cloud-based technologies and techniques.

Desired Technologies, techniques and frameworks:

  • Data warehousing with streaming and batch processing leveraging Data Lake solutions.

  • Metadata and schema design and management.

  • Data correlation and enrichment best practices.

  • Data science analytics dataset management.

  • REST, API-based web services.

  • Data-centered analytics development using technologies.

Apply