Data Architect

Skills

Data engineering data architect and data modelling dataspace data warehousing data plans data mesh AI experience is must have AWS Redshift Whipstock Windsor AI tools

Description

1. Data Architecture: In-depth knowledge and expertise in designing and implementing enterprise-level data architectures, including conceptual, logical, and physical data models.
2. Data Management: Strong understanding of data management principles, including data governance, data quality, data integration, data security, and data lifecycle management.
3. Database Technologies: Proficiency in working with various database technologies such as relational databases (e.g., Oracle, SQL Server, MySQL), NoSQL databases (e.g., MongoDB, Cassandra), and cloud-based data storage solutions (e.g., Amazon Redshift, Google BigQuery, Azure Cosmos DB).
4. Data Integration: Experience in designing and implementing data integration strategies and solutions, including ETL (Extract, Transform, Load) processes, data pipelines, and data streaming.
5. Data Modeling: Expertise in data modeling techniques and tools, such as ER modeling, dimensional modeling, and UML (Unified Modeling Language).
6. Data Governance and Compliance: Knowledge of data governance frameworks and practices, as well as an understanding of data compliance regulations (e.g., GDPR, CCPA) and industry-specific requirements (e.g., HIPAA for healthcare).
7. Data Security: Familiarity with data security principles and techniques, including data encryption, access control, data masking, and data anonymization.8. Business Acumen: Ability to understand and align data architecture with business objectives, requirements, and processes. Strong business analysis skills to identify and translate business needs into data architecture solutions.
9. Communication and Collaboration: Excellent communication and interpersonal skills to effectively communicate complex technical concepts to both technical and non-technical stakeholders. Ability to collaborate with cross-functional teams and work in an agile environment.
10. Industry Knowledge: Awareness of industry trends, emerging technologies, and best practices in data architecture, big data, data analytics, and data management.
11. Leadership and Management: Experience in leading and mentoring a team of data architects and data engineers. Ability to provide technical guidance and strategic direction for data-related initiatives.
12. Problem-solving and Analytical Skills: Strong problem-solving and analytical skills to analyze complex data requirements, identify data-related issues, and propose innovative solutions.