The Role
Design, build, automate and operate Snowflake-based AI/ML data platform components. Implement Snowpark workloads, data/feature pipelines, CI/CD and IaC, optimize compute/cost, monitor performance, and collaborate with data and ML teams to enable production AI solutions.
Summary Generated by Built In
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 : Machine Learning Operations
Minimum 5 year(s) of experience is required
Educational Qualification : 15 years full time education
Role Summary / Description
AI Powered Tech Talent
As a hands-on Engineer in AI Infrastructure Architecture, you will design, build, automate, monitor and optimize Snowflake-based AI/ML infrastructure for secure data access, feature preparation, model enablement, AI application integration and production analytics workloads. you will work on moderately complex platform components under guidance from senior architects and engineers, contributing to compute optimization, deployment automation, observability, governance, security and operational reliability for AI-driven business solutions.
Key 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.
Required 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
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 : Machine Learning Operations
Minimum 5 year(s) of experience is required
Educational Qualification : 15 years full time education
Role Summary / Description
AI Powered Tech Talent
As a hands-on Engineer in AI Infrastructure Architecture, you will design, build, automate, monitor and optimize Snowflake-based AI/ML infrastructure for secure data access, feature preparation, model enablement, AI application integration and production analytics workloads. you will work on moderately complex platform components under guidance from senior architects and engineers, contributing to compute optimization, deployment automation, observability, governance, security and operational reliability for AI-driven business solutions.
Key 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.
Required 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
About Accenture
Accenture is a leading global professional services company that helps the world’s leading businesses, governments and other organizations build their digital core, optimize their operations, accelerate revenue growth and enhance citizen services—creating tangible value at speed and scale. We are a talent- and innovation-led company with approximately 791,000 people serving clients in more than 120 countries. Technology is at the core of change today, and we are one of the world’s leaders in helping drive that change, with strong ecosystem relationships. We combine our strength in technology and leadership in cloud, data and AI with unmatched industry experience, functional expertise and global delivery capability. Our broad range of services, solutions and assets across Strategy & Consulting, Technology, Operations, Industry X and Song, together with our culture of shared success and commitment to creating 360° value, enable us to help our clients reinvent and build trusted, lasting relationships. We measure our success by the 360° value we create for our clients, each other, our shareholders, partners and communities.Visit us at www.accenture.com
Equal Employment Opportunity Statement
We believe that no one should be discriminated against because of their differences. All employment decisions shall be made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, military veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other basis as protected by applicable law. Our rich diversity makes us more innovative, more competitive, and more creative, which helps us better serve our clients and our communities.
Skills Required
- Minimum 5 years of overall experience
- Bachelor's degree in Computer Science, Computer Engineering, IT or related field (15 years full-time education)
- Hands-on experience with Snowflake data warehouse (warehouses, databases, schemas, secure access, data sharing)
- Experience with Snowpark and Snowflake Python/SQL workloads
- Proficiency in programming/scripting (Python, Java, C++, Bash or PowerShell) with minimum 2 years practical experience
- Proven experience designing or operating scalable data pipelines, feature preparation and ML-enablement workloads
- Working knowledge of dbt and Terraform (infrastructure-as-code)
- Experience with CI/CD pipelines, Git, containers and Kubernetes
- Experience with operational monitoring, performance optimization, cost-efficiency and security for data platforms
- Minimum 2 years experience coding, building, monitoring or troubleshooting AI/ML infrastructure or model deployment pipelines
- Understanding of MLOps/data platform patterns (feature engineering, model I/O management, monitoring and governance)
- Experience with cloud storage integrations and workflow orchestration tooling
- Machine Learning Operations (MLOps)
- Snowflake certifications (SnowPro Core, Advanced Architect, Data Engineer) or related platform credentials
- Familiarity with vector search, retrieval pipelines, Cortex/AI features and feature engineering patterns
- Exposure to industry use cases (BFSI, healthcare, retail/e-commerce, telecom, manufacturing, public sector) with compliance and data-governance expectations
Accenture Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Accenture and has not been reviewed or approved by Accenture.
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Healthcare Strength — Pay is considered competitive when paired with robust insurance options and other perks that compare well with large consulting and IT services peers. Multiple national medical plan options plus dental and vision are positioned as a core strength of the overall package.
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Retirement Support — Retirement support is positioned as a standout feature through a 401(k) dollar-for-dollar match up to a set percentage after eligibility. The package is reinforced by additional financial programs such as savings tools and related resources.
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Parental & Family Support — Parental and caregiving supports are presented as a meaningful benefit differentiator through substantial paid parental leave and multiple caregiver-oriented programs. Backup care and fertility/adoption/surrogacy navigation and reimbursements add breadth to family support beyond leave alone.
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The Company
What We Do
Accenture is a global professional services company with leading capabilities in digital, cloud and security. Combining unmatched experience and specialized skills across more than 40 industries, we offer Strategy and Consulting, Interactive, Technology and Operations services—all powered by the world’s largest network of Advanced Technology and Intelligent Operations centers. Our 500,000+ people deliver on the promise of technology and human ingenuity every day, serving clients in more than 120 countries. We embrace the power of change to create value and shared success for our clients, people, shareholders, partners and communities. Visit us at www.accenture.com.
