Snowflake Architect(Job No 911)

Solution Architect role.

Key responsibilities

• Data and AI Platform Design and Architecture:
  • Design and architect end-to-end solutions for data platforms, integrating data lakes, warehouses, and advance analytics capabilities, primarily using Databricks, Snowflake and AWS cloud, aligning to business requirement emphasizing security, compliance, and governance.
  • Create and maintain technical capability models and architecture blueprints to guide current and future-state implementations.
  • Develop and maintain reference architectures, blueprints, and solution design patterns for data platform.
  • Guide the integration of modern database technologies, including SQL and NoSQL databases.
  • Design ML platform solutions, to deliver end-to-end ML lifecycle, from exploration to deployment to serving and monitoring.
  • Design scalable and modular Gen AI platform solutions that integrate with existing data ecosystems on AWS.
  • Define reference architectures for implementing LLMs, retrieval-augmented generation (RAG), and prompt engineering.
  • Architect and oversee the technical implementation of a data and AI platform and applications, in line with compliance, governance and security standards.
  • Guide the adoption of MLOps and LLMOps best practices for Gen AI, ensuring efficient deployment, monitoring, and lifecycle management of AI models.
  • Ensure the governance and security principles established by the PMI Infosec team are adhered in all data and AI platform solutions, ensuring compliance with PMI IT policies and standards.
  • Conduct regular security reviews in collaboration with the security team and submit architectural designs to the review board for evaluation and approval.

• Collaboration and Communication:
  • Collaborate with business and technical stakeholders to gather requirements and define strategies for data platform modernization.
  • Work closely with cross-functional teams to ensure successful implementation of architectural solutions.
• Technology and Vendor assessment:
  • Participate in evaluation and recommendation of emerging data and AI technologies, leading proof-of-concept initiatives, and pilot projects, ensuring alignment with business and technology platform needs.
• Best Practices and Standards:
  • Establish and enforce Data platform best practices, standards, and guidelines.
  • Collaborate with different Architectures teams (Enterprise, solution, data, and security) to establish design pattern factory and take lead on democratizing the design patterns for reusability.
  • Create and deliver reusable technical assets and design patterns that help to accelerate the adoption of data and AI solution on AWS platform.
• Architectural Guidance and Documentation:
  • Create and maintain comprehensive documentation for all data and AI platform solution.

What we need

• Technical Proficiency:
  • Extensive hands-on experience in Big Data, Distributed Systems, and Cloud Architectures, particularly on AWS, leveraging SaaS, IaaS, and PaaS offerings.
  • Minimum 3 years’ experience in designing data platforms using Databricks.
  • Architecture experience with Snowflake, dbt and Matillion is also desired.
  • Experience and good understanding on Cloud infrastructure, networking and IAM services on AWS.
  • Proven experience in designing AI/ML platform solutions, including ML lifecycle management, MLOps and Gen AI
  • Familiarity with generative AI capabilities, patterns, and technology stacks, including emerging trends.
• Database Knowledge:
  • Proficient with relational databases and NoSQL databases, such as Document DB and Graph DB.
  • Skilled in working with both structured and unstructured data.
• Integration:
  • Strong knowledge of data integration methods, including messaging, queuing, change data capture, and data virtualization.
• BI/Visualization
  • Nice to have experience on BI tools like – Power BI, Graph Visualization reporting.
• Data Concepts and Management:
  • Solid understanding of data lake, data mesh, and data fabric architectures.
  • Hands on experience in data ingestion, processing, metadata management, data security, privacy, and governance.
• Architectural Expertise:
  • Proficient in architecting and documenting data and AI platform solutions at various levels using solution architecture methodologies and best practices.
• Communication Skills:
  • Excellent oral and written communication skills, with the ability to effectively convey technical concepts to both technical and business audiences.