24 days ago
Bengaluru, IndiaStaff+
Responsibilities
- Define Snowflake AI infrastructure vision, strategy, reference architecture, standards, and implementation approaches for enterprise AI/ML and data platforms.
- Make architecture 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 data and feature pipelines, Snowpark workloads, model integration patterns, and AI application environments.
- Define infrastructure-as-code patterns, delivery approaches, data/model enablement patterns, monitoring strategies, SLAs/SLOs, and cost/performance governance.
- Lead architecture assessments and design reviews, validating findings through hands-on implementation, profiling, tuning, and troubleshooting.
- Evaluate Snowflake AI, vector search, governance, interoperability, and data application capabilities for enterprise solutions.
- Provide executive and client-level technical recommendations tied to business outcomes.
- Mentor architects and Technical Architects and promote community best practices.
Requirements
- Bachelor's degree in Computer Science, Computer Technical Architecting, Information Technology, or a related technical field.
- Minimum 15 years of experience is stated; the role also requires at least 6 years building, monitoring, troubleshooting, designing, and operating AI/ML infrastructure, cloud platforms, data platforms, model deployment pipelines, or large-scale architecture solutions.
- At least 6 years of proficiency with programming or scripting languages such as Python, Java, C++, Bash, PowerShell, or equivalent languages.
- Expert hands-on architecture experience with Snowflake warehouses, databases, schemas, secure data sharing and access controls, Snowpark, Cortex/AI capabilities, Streams/Tasks, Streamlit, and cloud ecosystem integrations.
- Strong knowledge of scalable data architecture, AI feature and data pipelines, model integration, governance, optimization, observability, and resilience.
- Experience with SQL, Python, dbt, Terraform, monitoring, security guardrails, and platform cost optimization.
- Experience with workflow and pipeline tools such as Apache Airflow, Kubeflow, managed orchestration services, or platform-native tooling.
- Experience leading AI infrastructure projects and teams, including technical direction, design reviews, delivery governance, and stakeholder alignment.
- Experience applying MLOps, DataOps, and InfraOps practices to production pipelines, model enablement, monitoring, incident response, and rollback strategies.
- Ability to evaluate Snowflake architecture options and produce standards, patterns, decision records, benchmarks, and executive-ready recommendations.
- Strong project management, communication, problem-solving, and cross-functional collaboration skills.
- Experience evaluating and selecting AI technologies, frameworks, reference architectures, and platform services for production solutions.
- Preferred qualifications include Snowflake certifications such as SnowPro Advanced Architect or SnowPro Advanced Data Architect, enterprise data and AI infrastructure experience in regulated industries, and experience with governance, secure data sharing, enterprise architecture roadmaps, vendor management, FinOps, and production support.
Categories
Data Engineering
About Accenture
Accenture is a global professional services firm providing management consulting, systems integration and technology, cybersecurity, and business process outsourcing for enterprises and governments. It operates a services-driven model delivering projects and managed services, often with major cloud and software partners, across industries worldwide. Headquartered in Dublin and publicly traded on the NYSE (ACN), it originated as Andersen Consulting and adopted the Accenture name in 2001.