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 : Snowflake Data Warehouse
Good to have skills : AI Agents & Workflow Integration
Minimum 18 year(s) of experience is required
Educational Qualification : 15 years full time education

Role Summary / Description

AI Powered Tech Talent

As a Technical Architect in AI Infrastructure Architecture, you will act as a senior technical authority for Snowflake-based AI/ML and data platform infrastructure, shaping the technical vision, reference architecture, standards and implementation strategy for enterprise AI systems. You will evaluate complex choices across warehouses, secure data architecture, Snowpark, AI application enablement, feature/data pipelines, governance, observability, security and cost optimization, while guiding senior and lead architects/Technical Architects to deliver resilient, scalable and production-ready AI infrastructure. You will bring industry experience across enterprise AI adoption, compliance, reliability, FinOps and platform modernization to help clients translate AI infrastructure trade-offs into measurable business value.

Key Responsibilities
Set the overarching Snowflake AI infrastructure vision, strategy and reference architecture for enterprise AI/ML and data platform systems, including compute, storage, governance, model enablement, integration and observability.
Own complex architectural decisions across Snowflake warehouses, databases, schemas, secure data sharing/access controls, Snowpark, Streams/Tasks, Cortex/AI capabilities, Streamlit and cloud integrations, rationalizing options against client standards and business objectives.
Architect and prototype cost-optimized AI-enabling data/feature pipelines, Snowpark workloads, model integration patterns and AI application environments, building benchmarks, proof-of-concepts and reusable implementation patterns.
Define architecture standards, reusable infrastructure-as-code patterns, CI/CD approaches, data/model enablement patterns, monitoring strategy, SLAs/SLOs and cost/performance governance for production AI systems.
Lead architecture assessments and design reviews validate findings through hands-on implementation, profiling, performance tuning and troubleshooting across warehouses, queries, data pipelines, access controls and integrations.
Evaluate emerging Snowflake AI, vector search, governance, interoperability and data application capabilities, and recommend where they belong in enterprise solutions.
Provide executive and client-level technical advisory, translating platform trade-offs into clear, defensible recommendations connected to business outcomes.
Mentor architects and Technical Architects, build community best practices and represent the practice in internal and external technical forums.

Required Qualifications
Bachelor's degree in Computer Science, Computer Technical Architecting, Information Technology or a related Technical Architecting field.
Minimum 6 years of experience coding, building, monitoring, troubleshooting, designing and operating AI/ML infrastructure, cloud platforms, data platforms, model deployment pipelines or large-scale Technical Architecting solutions.
Strong understanding of AI/ML concepts and the compute, infrastructure, orchestration and deployment foundations required to run production AI systems.
Minimum 6 years of proficiency in programming or scripting languages such as Python, Java, C++, Bash, PowerShell or equivalent Technical Architecting languages.
Experience with data pipeline and workflow management tools such as Apache Airflow, Kubeflow, managed orchestration services or platform-native workflow tooling.
Proven experience leading AI infrastructure projects and teams, including technical direction, design reviews, delivery governance and stakeholder alignment.
Strong project management, communication, problem-solving and cross-functional collaboration skills in fast-paced client or enterprise environments.
Demonstrated experience evaluating and selecting AI technologies, frameworks, reference architectures and platform services for production solutions.

Required Skills/ Experience
Expert-level hands-on architecture experience with Snowflake warehouses, databases, schemas, secure data sharing/access controls, Snowpark, Cortex/AI capabilities, Streams/Tasks, Streamlit and cloud ecosystem integrations.
Deep knowledge of scalable data architecture, AI feature/data pipelines, model integration patterns, governance, query/warehouse optimization, observability and resilience Technical Architecting.
Strong experience with SQL, Python, dbt/Terraform, CI/CD, security guardrails, monitoring and platform cost optimization.
Ability to evaluate multiple Snowflake architecture options and produce standards, patterns, decision records, benchmarks and executive-ready recommendations.
Experience applying MLOps/DataOps/InfraOps practices for production data/AI pipelines, model enablement, monitoring, incident response and rollback strategies.

Good to Have Skills
Snowflake certifications such as SnowPro Advanced Architect, SnowPro Advanced Data Technical Architect or related AI/data platform credentials.
Industry experience designing enterprise data and AI infrastructure for BFSI, healthcare, retail/e-commerce, telecom, manufacturing, energy or public sector environments with compliance, security and reliability constraints.
Exposure to Snowpark, Cortex/AI features, vector search, retrieval pipelines, feature Technical Architecting, model enablement and AI application architecture.
Experience with data governance, secure data sharing, enterprise architecture roadmaps, vendor/partner management, FinOps and production support operating models.