Data & Ai Senior Architecture Consultant

**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 Data & AI Senior Architecture Consultant is an advanced level expert who proactively supports the delivery of multi-technology consulting services to clients by analysing and providing more strategies and solutions on all aspects related technology components.

This role works on the development of the architectural approach for one or more layer of a complex solution and reviews the developed components to ensure that it aligns to roadmaps and adherence to architecture principles.

This role deals with clients who have complex information technology environments.

**What you'll be doing**

**Key Responsibilities**:

- ** Strategic Leadership**: Shapes technological strategies and development process.
- ** Technology Oversight**: Supervises current technologies and defines relevant policies.
- ** Technical Decision-Making**:Makes technology-related decisions aligned with business objectives.
- ** Technical** Leadership**: Support Software Development Team for the definition of guidelines for the development process.
- ** Hiring and Training**: Involved in hiring, training, onboarding.
- ** Customer relationship**: Builds and strengthens business relationships with partners and represents the company at conferences and events.
- ** Data and AI Strategy**: Creates and implements strategies to optimize data systems and technologies, evaluates processes and technologies to make meaningful outcome of data.
- ** G**o-To-**M**arket** strategy**: Participates in strategic planning and goal setting for the Go-To-Market (GTM) strategy.
- ** Consulting**: Acts as a consulting partner for our clients as part of the Data & AI GTM Stream.

**Knowledge and Attributes**:

- ** IT Industry and **Technology**: Extensive understanding of IT industry environment and business needs, extensive understanding of current technologies (see list below) and technology trends and the ability to define relevant policies.
- ** Enterprise **Architecture**: Knowledge and practical experience with at least one Enterprise Architecture Framework like TOGAF, Zachman, Gartner Enterprise Architecture Framework etc.
- ** Project Management**: Knowledge of modern PM methodologies like Agile methodologies - Scrum, Kanban, Hybrid methodologies - Agile-Waterfall Hybrid, Scaled Agile Framework (SAFe), etc.
- ** Consulting** Practices**: Knowledge of consulting practices like for example Data Strategy Development, AI Readiness Assessment, Data Architecture Design, AI Solution Development, Data Analytics Consulting, Tool and Platform Assessment, etc.
- Technical skills:

- ** Data Engineering**:

- **_ ETL Processes:_**_ Experience with Extract, Transform, Load (ETL) processes and _supporting tools_, for example_ Apache _NiFi_, Talend, or Informatica._
- **_ Data Warehousing:_**_ Knowledge of data warehousing solutions like Amazon Redshift, Google _BigQuery_, or Snowflake._
- **_ Data Lakes:_**_ Understanding of data lake architectures and technologies such as Apache Hadoop and Apache Spark._
- **_ Data Spaces:_**_ _Understanding of data spaces concept and _familiarity with data spaces design principles_ and corresponding open source components_._
- ** Machine Learning and AI**:

- **_ Frameworks:_**_ _Knowledge of_ machine learning frameworks such as TensorFlow, _PyTorch_, and Scikit-Learn._
- **_ Model Deployment:_**_ Experience with deploying machine learning models using platforms like TensorFlow Serving, _MLflow_, or Kubernetes._
- **_ AI Services:_**_ Familiarity with AI services provided by cloud platforms, such as AWS SageMaker, Azure Machine Learning, and Google AI Platform._
- ** Cloud Platforms**:

- **_ AWS:_**_ Knowledge of AWS services including EC2, S3, Lambda, RDS, and SageMaker._
- **_ Azure:_**_ _Knowledge of _Azure services such as Azure Data Factory, Azure Machine Learning, and Azure Synapse Analytics._
- **_ Google Cloud:_**_ Familiarity with Google Cloud services like _BigQuery_, AI Platform, and Dataflow._
- ** Big Data Technologies**:

- **_ Hadoop Ecosystem:_**_ Understanding of Hadoop ecosystem components such as HDFS, MapReduce, Hive, and Pig._
- **_ Real-Time Data Processing:_**_ Experience with real-time data processing frameworks like Apache Kafka, Apache Flink, or Apache Storm._
- **_ NoSQL Databases:_**_ Knowledge of NoSQL databases such as MongoDB, Cassandra, and HBase._
- ** Data Governance and Security**:

- **_ Data Privacy:_**_ Understanding of data privacy regulations such as GDPR and CCPA._
- **_ Data Security:_**_ Experience with data security practices, including encryption, access control, and secure data sharing._
- **_ Data Quality:_**_ Knowledge of data qu