Minimum qualifications:
- Bachelor's degree in Computer Science, a related technical field, or equivalent practical experience.
- 10 years of experience in cloud computing, with a focus on data architecture, data analytics, and data engineering.
- Experience with modern application development and DevOps practices, including CI/CD, containerization (Docker, Kubernetes), and infrastructure-as-code.
- Experience in programming/scripting languages such as Python, with developing and deploying data solutions.
- Experience in architecting and developing software or infrastructure for scalable, distributed systems.
- Experience with database technologies (SQL, NoSQL), streaming data, and data warehousing solutions.
Preferred qualifications:
- 10 years of experience in cloud computing, with a focus on data architecture, data analytics, and data engineering, in a customer-facing or consulting role.
- Experience in understanding customer requirements, breaking them down into actionable components, and designing technical architectures to meet those needs.
- Experience managing stakeholder expectations and building consensus around complex technical projects.
- Ability to analyze complex business problems and develop innovative technical solutions leveraging GenAI and data.
- Ability to build rapid prototypes and proof-of-concepts to demonstrate innovative solutions.
- Ability to communicate complex technical concepts effectively to both technical and non-technical audiences.
Responsibilities:
- Lead the design, development, and iterative refinement of data-centric and AI-powered solutions on Google Cloud Platform (GCP), showcasing the potential of data and AI to address specific business needs.
- Collaborate with customers to understand their business challenges, technical requirements, and objectives. Build strong, trusted relationships with customers and stakeholders.
- Mentor and guide junior team members, fostering a culture of innovation and continuous learning.
- Establish and promote innovative best practices and methodologies for data-driven solutions, actively contributing to industry thought leadership through publications, presentations, and community engagement.
