Data Architect

Responsibilities:

• Data Architecture Design: Develop and maintain comprehensive data architectures, including data models, data warehouses, and data lakes, to support business operations and analytics.

• Database Design: Design and implement efficient database schemas and structures to ensure data integrity, security, and optimal performance.

• Data Integration: Develop and manage data integration strategies and processes to ensure seamless data flow between systems and applications.

• Data Governance: Establish and enforce data governance policies and practices to ensure data quality, consistency, and compliance with regulations.

• Cloud Expertise: Utilize cloud technologies (e.g., AWS, Azure, Google Cloud) to architect scalable and secure data solutions. Experience with cloud-native data services (e.g., AWS Redshift, Azure Synapse, Google BigQuery) is essential.

• Collaboration: Work closely with business analysts, data scientists, and IT teams to understand data needs and translate them into effective data solutions.

• Performance Optimization: Monitor and optimize database performance, including query optimization, indexing, and data storage management.

• Technology Evaluation: Evaluate and recommend new data technologies and tools to enhance data architecture and analytics capabilities.

• Documentation: Create and maintain comprehensive documentation for data architecture, data models, and data integration processes.

• Education: Bachelor s degree in Computer Science, Information Systems, Data Science, or a related field. Advanced degrees or certifications (e.g., TOGAF, Certified Data Management Professional) are a plus.

• Experience: Minimum of 8 years of experience in data architecture, data modeling, and database design, with a proven track record of successfully implementing data solutions.

• Expertise in database technologies (e.g., SQL, NoSQL, cloud databases).

• Strong knowledge of data modeling techniques and tools (e.g., ERwin, IBM InfoSphere Data Architect).

• Proficiency in data integration tools and techniques (e.g., ETL, ELT) and cloud-based data platforms (e.g., AWS, Azure, Google Cloud)

• Experience with data warehousing solutions and data lakes.

• Familiarity with big data technologies (e.g., Hadoop, Spark) is a plus.

• Excellent problem-solving and analytical skills.

• Strong communication and interpersonal skills, with the ability to collaborate effectively with cross-functional teams.

• Knowledge of data governance and data privacy regulations