Employment type in the employer’s posting: Full timeFrom the employer’s posting
Set the Snowflake AI infrastructure vision and reference architecture
Own complex architecture decisions across Snowflake warehouses, Snowpark and Cortex
Prototype cost-optimized AI data pipelines and Snowpark workloads
Define architecture standards, CI/CD approaches and monitoring strategy
Lead architecture assessments and design reviews
Set the overarching Snowflake AI infrastructure vision, strategy and reference architecture for enterprise AI/ML and data platform systems
Own complex architectural decisions across Snowflake warehouses, databases, schemas, secure data sharing/access controls, Snowpark, Streams/Tasks, Cortex/AI capabilities, Streamlit and cloud integrations
Architect and prototype cost-optimized AI-enabling data/feature pipelines, Snowpark workloads, model integration patterns and AI application environments
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
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.
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Day 25
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Well past Accenture’s usual: its jobs come down by about day 1.
From 9,462 similar postings that came down, and 2,606 of Accenture’s. Coming down is not always a hire.
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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.
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.
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.
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. 15 years full time education
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Yes. We’ve followed 24 other Data Platform Architect jobs there since September 2026. All 24 are still up.
Large employers often post one title for several teams, so these can be separate openings.
New Data Leadership postings at the employers we follow are about the same over the last 6 weeks compared with the 6 before.
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We file this job under Data Leadership, by the work the posting describes.
Data leaders run data teams or design how a company's data fits together. Managers hire, set priorities and answer for whether the data work pays off; architects choose the systems and the shape of the data.
Expect years of hands-on data work, experience leading people or projects, and the ability to explain tradeoffs to executives.
Unlike a senior engineer or analyst, most of a data leader's week goes to people, planning and decisions rather than building.
The government has no code for data leaders, so these figures are for the closest group, computer and information systems managers, which includes data processing and data operations managers.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 11-3021.
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
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