
Data Solutions Architect
3 days ago
About the Role:
- We are seeking an experienced Data Analyst to join our team. This role involves designing, building, and deploying analytics solutions that enhance innovation and deliver business value.
Key Responsibilities:
- Design, develop, and maintain end-to-end analytics solutions in collaboration with cross-functional teams.
- Write high-quality, maintainable, and well-documented code adhering to best software engineering standards.
- Develop and optimize data pipelines, ensuring efficient and secure integration from varied sources.
- Build intelligent, API-driven services for diverse business use cases using Python and modern AI frameworks.
- Utilize tools and libraries like Pandas and Pydantic for building robust data pipelines.
- Implement machine learning and analytics solutions with monitoring for model performance, data drift, and system observability.
- Manage deployment using containerization and workflow orchestration tools.
- Integrate services with cloud platforms and develop microservices architectures with scalable RESTful or GraphQL APIs.
- Ensure solutions comply with security, privacy, and responsible AI standards.
- Keep up-to-date with trends and best practices in AI, analytics, and software development.
Requirements:
- Bachelor's or Master's degree in Computer Science, Engineering, or related fields.
- Strong problem-solving ability and capability to communicate technical information to non-technical stakeholders.
- Proficient in software engineering principles and experienced with multiple programming languages such as Python, Java, or JavaScript.
- Hands-on experience with AI/ML frameworks and building production-grade data pipelines following DataOps and MLOps practices.
- Familiarity with containerization (Docker, Kubernetes) and CI/CD pipeline automation.
- Experience with modern web development and building scalable APIs.
- Knowledge of cloud platforms (preferred but not specific).
- Understanding of security, privacy, and regulatory compliance within AI/ML contexts.
- Prior exposure to regulated industries or financial services is a plus.
- Comfortable using data visualization and team collaboration tools.
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