Data Platform Architect

About Accenture

Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, Information Technology or a related engineering field.
• Minimum 2 years of experience coding, building, monitoring or troubleshooting AI/ML infrastructure, data platforms, model deployment pipelines or cloud/platform engineering solutions.
• Strong understanding of AI/ML concepts and the compute, storage, networking, security and deployment foundations required to run AI workloads.
• Minimum 2 years of proficiency in programming or scripting languages such as Python, Java, C++, Bash or PowerShell.
• Experience with CI/CD, infrastructure-as-code, containers, Kubernetes, workflow orchestration and operational monitoring tools.
• Strong problem-solving ability, communication skills and collaboration mindset in a fast-paced engineering environment.
• Required Skills/ Experience
• Hands-on experience with Snowflake warehouses, databases, schemas, secure data sharing/access controls, Snowpark, Python/SQL workloads and cloud storage integrations.
• Experience designing or operating scalable data pipelines, feature preparation workloads, AI application integrations and production analytics or ML enablement workloads.
• Working knowledge of SQL, Python, dbt/Terraform, CI/CD pipelines, data observability and cost/performance optimization practices.
• Ability to optimize warehouses, queries, data pipelines and integrations for performance, reliability, scalability, cost and security.
• Understanding of MLOps/data platform patterns including feature engineering, model input/output management, monitoring and governance.
• Good to Have Skills
• Snowflake certification such as SnowPro Core, SnowPro Advanced Architect, SnowPro Data Engineer or related platform credentials.
• Exposure to industry use cases in BFSI, healthcare, retail/e-commerce, telecom, manufacturing or public sector where data/AI platforms must meet compliance, reliability and data-governance expectations.
• Familiarity with Snowpark, Cortex/AI features, vector search, retrieval pipelines, feature engineering and model-enablement patterns.
• Knowledge of data governance, data sharing controls, FinOps practices, incident management and production support processes for enterprise AI platforms.
• 15 years full time education

Responsibilities

• Write, review and debug SQL, Python, scripts and infrastructure-as-code for Snowflake AI/ML infrastructure, automation, monitoring and deployment tooling.
• Configure and manage Snowflake warehouses, databases, schemas, secure access patterns, Snowpark workloads, Streamlit apps, model-related data pipelines and integrations with cloud storage and orchestration tools.
• Support deployment automation and CI/CD pipelines for Snowflake-based AI solutions using tools such as Git, Terraform, dbt, Python, containers and workflow orchestration tooling where applicable.
• Deploy and operate data/feature pipelines, AI application integrations and model-enablement components while applying reliability, security, cost-efficiency and scalability practices.
• Monitor warehouse utilization, query performance, pipelines and integration health troubleshoot issues across compute, storage, access control, data movement and application layers.
• Collaborate with data scientists, ML engineers, data engineers, platform engineers and architects to integrate Snowflake-enabled AI solutions into enterprise systems while meeting compliance and operational requirements.
• Document reusable patterns, configuration standards and runbooks for Snowflake-based AI infrastructure.