Data Platform Architect

About Accenture

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.
• 15 years full time education

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.