Enterprise Architect

Make an impact with NTT DATA
Join a company that is pushing the boundaries of what is possible. We are renowned for our technical excellence and leading innovations, and for making a difference to our clients and society. Our workplace embraces diversity and inclusion – it’s a place where you can grow, belong and thrive.

Your day at NTT DATA

The Enterprise cloud architect is to be responsible for maintaining architecture for the Client organization’s software, hardware, and applications needs. An important task in this role is establishing best cloud practices across the client cloud center of excellence and SRE teams. Success in this role will be displayed by identifying and selecting the most cost-efficient cloud providers while ensuring and easy IaC and DevOps practices are in place. Develop and maintain understanding of key processes, schedules, cycles, profiles, etc. for the technical systems in use by a customer; Make presentations with pre-sales to to clients, and peer groups as requested.

What you'll be doing

Key Responsibilities:

• Manages and coordinates the development of processes for effective data analysis and reporting.
• Provides guidance on cross-functional projects using advanced data modelling and analysis techniques to discover insights that will guide strategic decisions and uncover optimization opportunities.
• Sets operational objectives for the development and implementation of comprehensive tools and strategies that allow raw data to be transformed into business insights.
• Manages data accuracy and consistent reporting by designing and creating optimal processes and procedures for analytics team to follow.
• Uses advanced data modelling, predictive modelling and analytical techniques to provide guidance to interpreting key findings from company data and leverage these insights into initiatives that will support business outcomes.
• Manages the building, development and maintenance of data models, reporting systems, data automation systems, dashboards and performance metrics support that support key business decisions.
• Collaborates with internal teams, external partners, and stakeholders to identify AI opportunities, build strategic partnerships, and leverage external expertise.
• Sets operational objectives for assessing the potential impact of AI on business processes, customer experience, and revenue generation, and guiding decision-making based on ROI and feasibility analysis.
• Guides on addressing ethical, legal, and regulatory considerations related to AI, ensuring compliance with data privacy and security requirements.
• Manages the design and delivery of reports and insights that analyze business functions and key operations and performance metrics.
• Manages and optimizes processes for data intake, validation, mining and engineering as well as modelling, visualization and communication deliverables.
• Reviews, interprets and reports results of analytical initiatives to stakeholders in leadership, technology, sales, marketing and product teams.
• Provides support, coaching and development opportunities to a team of data analytics professionals within their remit.

Knowledge and Attributes:

• Excellent knowledge of data mining principles, predictive analytics, mapping, collecting data from multiple data systems on premises and cloud-based data sources.
• Ability to collect, organize, analyze, and disseminate significant amounts of information with attention to detail and accuracy.
• Organized, strong analytical skills, and capable of handling multiple projects with senior stakeholders.
• Excellent knowledge of statistical analysis and reporting packages.
• Ability to identify solutions in sprawling data sets and the business mindset to convert insights into strategic opportunities.
• Ability to produce understandable and actionable reports gleaned from data.
• Problem solving, quantitative and analytical abilities.
• Excellent communication, collaboration and delegation skills.
• Programming skills with querying languages, SLQ, SAS, etc.
• Good management and leadership skills.

Academic Qualifications and Certifications:

• Bachelor's degree or equivalent in Data Science, Business Analytics, Mathematics, Economics, Statistics, Engineering, Computer Science, Computer Engineering or another quantitative field is preferred.

Required Experience:

• Extended demonstrated experience using statistical packages for analyzing datasets (Excel, PBI, R, Python etc.).
• Extended line manager experience managing end-to-end process improvement implementations spanning process, systems and people.
• Extended demonstrated experience with