Data Platform Architect

Data Platform Architect

Project Role : Data Platform Architect
Project Role Description : Architects the data platform blueprint and implements the design, encompassing the relevant data platform components. Collaborates with the Integration Architects and Data Architects to ensure cohesive integration between systems and data models.
Must have skills : Machine Learning Operations
Good to have skills : Snowflake Data Warehouse
Minimum 7.5 year(s) of experience is required
Educational Qualification : 15 years full time education

Role Summary / Description

AI Powered Tech Talent

As a Senior Engineer in AI Infrastructure Architecture for Snowflake, you will own significant portions of the end-to-end architecture and engineering of optimized data and AI infrastructure for production machine learning and AI-enabled applications. You will design scalable warehouses, Snowpark workloads, AI-ready data/feature pipelines, model-enablement patterns, automation and operational controls that align with client standards, SLAs, security, compliance and cost-efficiency expectations. You will bring industry experience across enterprise AI adoption, platform modernization, regulated data workloads, FinOps and production reliability, while mentoring engineers and partnering with architects to translate business requirements into robust Snowflake-based AI infrastructure solutions.

Key Responsibilities
Own end-to-end architecture and design of optimized Snowflake data and AI infrastructure, including warehouses, Snowpark workloads, secure data architecture, AI-ready feature/data pipelines, model integration and AI application enablement.
Design and tune scalable Snowflake warehouses, tasks, streams, Snowpark services, Cortex/AI capabilities, Streamlit applications and cloud integrations, including compute sizing, query optimization, governance, access controls and high-throughput data access design.
Serve as an authoritative AI infrastructure expert on Snowflake, applying deep knowledge of Snowflake Data Cloud capabilities, data/AI application patterns, governance, security and cost levers.
Develop and evaluate architecture alternatives, weighing trade-offs across compute, networking, storage, orchestration, model serving, observability, security, compliance, cost and operational complexity.
Lead architecture assessments and reviews of existing and proposed environments, identifying gaps, risks, bottlenecks and optimization opportunities, and recommending remediation actions.
Drive architecture decision-making by documenting rationale, trade-offs, assumptions and dependencies so decisions are transparent, defensible and aligned with business SLAs and standards.
Define and maintain AI infrastructure roadmap inputs, capacity planning models, scaling strategies, cost forecasts and performance improvement opportunities.
Design deployment, automation and CI/CD strategies for reliable, repeatable and scalable releases of AI systems, models, data pipelines and platform components into production.
Establish AI monitoring and observability practices across InfraOps and MLOps, including SLAs, SLOs, alerting, performance/cost tracking and continuous optimization.
Integrate AI/ML systems into enterprise environments while ensuring interoperability, security, compliance, regulatory alignment and adherence to client standards.
Collaborate with clients, stakeholders, architects and engineering teams to align infrastructure decisions with business outcomes and translate requirements into actionable architecture standards.
Set technical direction for workstreams, mentor engineers, review designs/code and promote engineering best practices across the team.

Required Qualifications
Bachelor's degree in Computer Science, Computer Engineering, Information Technology or a related engineering field.
Minimum 4 years of experience coding, building, monitoring, troubleshooting, designing and operating AI/ML infrastructure, cloud platforms, data platforms, model deployment pipelines or large-scale engineering solutions.
Strong understanding of AI/ML concepts and the computing infrastructure required to deploy, run and optimize production AI workloads.
Minimum 4 years of proficiency in programming or scripting languages such as Python, Java, C++, Bash, PowerShell or equivalent engineering languages.
Experience with data pipeline and workflow management tools such as Apache Airflow, Kubeflow, managed orchestration services or platform-native workflow tooling.
Strong problem-solving skills and ability to work in a fast-paced engineering or client delivery environment.
Excellent communication, collaboration and stakeholder alignment skills.
Minimum 4 years of experience in AI/ML infrastructure engineering or related roles on a hyperscaler or enterprise platform for deploying large-scale solutions.
Proven experience leading AI projects or engineering workstreams and managing priorities across multiple initiatives.
Demonstrated experience evaluating and selecting AI technologies, frameworks, cloud services and architecture patterns.

Required Skills/ Experience
Strong hands-on experience with Snowflake warehouses, databases, schemas, secure data sharing/access controls, Snowpark, Cortex/AI capabilities, Streams/Tasks, Streamlit and cloud ecosystem integrations.
Experience architecting scalable data and feature pipelines, AI application integrations, model-enablement patterns, governance, query/warehouse optimization and secure data access.
Strong working knowledge of SQL, Python, dbt/Terraform, CI/CD, DataOps, MLOps/AI enablement patterns, observability and incident response practices.
Ability to optimize Snowflake AI infrastructure for performance, cost, scalability, security, reliability and compliance.
Experience producing architecture decision records, reference implementations, standards, runbooks and reusable platform patterns.

Good to Have Skills
Snowflake certifications such as SnowPro Advanced Architect, SnowPro Advanced Data Engineer or related AI/data platform credentials.
Industry experience in BFSI, healthcare, retail/e-commerce, telecom, manufacturing, energy or public sector environments where data/AI platforms must meet compliance, security, reliability and cost-control requirements.
Exposure to Snowpark, Cortex/AI features, vector search, retrieval pipelines, feature engineering, model enablement and AI application architecture.
Knowledge of Snowflake governance, secure data sharing, FinOps, infrastructure partner/vendor collaboration and production support operating models.