Data Architect

Job Summary:

The Data Architect is responsible for designing, structuring, and maintaining the data architecture that supports enterprise applications, analytics, and business intelligence. This role ensures the integrity, accessibility, and security of the organization's data assets and works closely with stakeholders, engineers, and analysts to implement scalable and efficient data systems.

Key Responsibilities:

• Design and implement enterprise-wide data architecture and data modeling strategies
• Develop and maintain data standards, data governance policies, and best practices
• Define data flows, integration strategies, and data storage solutions (e.g., data lakes, warehouses)
• Collaborate with data engineers, analysts, and business stakeholders to understand requirements
• Evaluate and recommend modern data technologies, platforms, and tools
• Ensure security, compliance, and quality of data across systems
• Support migration from legacy systems to modern cloud-based data platforms
• Define data cataloging and metadata management strategies
• Monitor and optimize performance of data pipelines and storage solutions

Required Skills and Qualifications:

• Bachelor's or Master's degree in Computer Science, Information Systems, or a related field
• 7+ years of experience in data management, architecture, or engineering
• Strong understanding of relational and non-relational databases (SQL, NoSQL)
• Experience with cloud platforms like AWS, Azure, or Google Cloud (BigQuery, Redshift, Snowflake, etc.)
• Proficiency in data modeling (conceptual, logical, physical) using tools like Erwin, Enterprise Architect, or dbt
• Solid knowledge of data integration (ETL/ELT), APIs, and streaming platforms (Kafka, Apache Spark, etc.)
• Familiarity with data governance, master data management (MDM), and data quality tools
• Strong problem-solving, communication, and stakeholder engagement skills
• Design and implement data architectures and models, focusing on data warehouses and Snowflake-based environments.
• Ensure that data is structured for efficient querying and analysis, aligning with business goals and performance requirements.