Data Architect
7 days ago
Responsibilities:
- Design, implement, and maintain scalable and efficient data architectures to support AI and automation projects.
- Collaborate with data scientists, AI researchers, and engineers to ensure data availability and quality for AI model training and deployment.
- Develop and optimize data pipelines and ETL processes to handle large volumes of structured and unstructured data.
- Ensure data integrity, security, and compliance with relevant regulations.
- Create and maintain comprehensive data models, schemas, and documentation.
- Evaluate and integrate new data technologies and tools to enhance our data infrastructure.
- Provide technical leadership and mentorship to data engineers and analysts.
- Stay current with industry trends and best practices in data architecture and AI.
Must-Have Qualifications
- Bachelor's degree in computer science, Information Systems, or a related field.
Proven experience as a Data Architect, Senior Data Engineer, or in a similar data focused role. - Minimum 8 years experience as a Data Architect, Senior Data Engineer, or in a similar data-focused role
- Strong knowledge of database management systems, both SQL (e.g., Azure SQL, PostgreSQL) and NoSQL (e.g., Cosmos DB, MongoDB).
- Proficiency in data modeling, data warehousing, and ETL/ELT pipelines using tools such as Apache Airflow, or Azure Data Factory.
- Experience with big data technologies such as Apache Spark, Databricks, or similar.
- Familiarity with cloud data platforms, preferably Microsoft Azure
- Strong understanding of data governance, data quality, and data security practices.
- Familiarity with containerization and orchestration tools like Docker and Kubernetes.
- Understanding of CI/CD pipelines and DevOps practices for data workflows.
- Knowledge of data privacy regulations such as GDPR, CCPA, or similar compliance frameworks.
Nice-to-Have Qualifications
- Experience with big data technologies such as Apache Spark, Databricks, or similar
Experience with AI and machine learning frameworks (e.g., TensorFlow, PyTorch, Azure ML). - Knowledge of data visualization tools (e.g., Power BI, Tableau, Looker) and how to support reporting layers.
- Exposure to real-time data processing and streaming technologies (e.g., Kafka, Azure Event Hubs, Stream Analytics).
- Hands-on experience with Python, PySpark, or similar scripting languages for data transformation.
- Certifications in Architecture
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