Data Architect

Data Architecture

• Design and maintain the company’s data architecture.
• Create data models for applications, reports, and analytics.
• Set standards for data structure, quality, metadata, and data lifecycle.

Technology Strategy

• Create a roadmap for data modernization, cloud, and AI/ML projects.
• Recommend suitable data technologies such as Data Lakes, Data Warehouses, and real-time data platforms.
• Keep updated with new data architecture technologies and trends.

Stakeholder Collaboration

• Work with business teams, data analysts, and data scientists to understand their data needs.
• Coordinate with application, infrastructure, cloud, and enterprise architecture teams.
• Make sure data solutions are scalable, reliable, and meet business requirements.

Data Governance & Compliance

• Create and implement data governance standards and policies.
• Ensure data privacy, security, quality, and compliance.
• Review data architecture to ensure proper data management and integrity.

Data Integration

• Design ways to connect and share data between different systems.
• Support integration between cloud, on-premise, and third-party systems.
• Design and maintain data APIs and data pipelines.

Security & Risk Management

• Work with cybersecurity teams to protect company data.
• Identify and reduce risks such as data loss, breaches, and system failures.
• Manage secure access and user permissions for data.

Improvement & Innovation

• Improve data storage, processing, and data flows.
• Support self-service analytics and trusted data.
• Build a strong data foundation for AI, machine learning, and advanced analytics.

Documentation

• Maintain data architecture documents, data dictionaries, models, and glossaries.
• Create and follow data architecture standards and best practices.

Requirements

• Bachelor’s degree in Computer Science, Data Science, Information Systems, or related field.
• At least 8 years of experience in Data Architecture, Data Engineering, or Data Management.
• Experience with Data Lakes, Data Warehouses, and large-scale data platforms.
• Strong experience in data modeling, ETL/ELT, data integration, and metadata management.
• Knowledge of SQL, Python, Spark, Kafka, DBT, or similar technologies.
• Experience with platforms such as Snowflake, AWS Redshift, or Google BigQuery.
• Strong analytical, problem-solving, communication, and collaboration skills.
• Certifications such as AWS, Azure, Google Cloud, CDMP, or TOGAF are an advantage.