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Accenture

Advises businesses and runs their technology

Data Platform Architect

Data EngineeringUpdated on Nov 11
Full-timePune, Maharashtra, IndiaNo salary listedIn-person

About the Job

Requirements

  • A bachelor's degree in Computer Science, Computer Technical Architecting, Information Technology, or a related technical field is required.
  • A minimum of 18 years of experience is required.
  • A minimum of 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 architecture solutions is required.
  • Strong understanding of AI/ML concepts and the compute, infrastructure, orchestration, and deployment foundations needed to run production AI systems is required.
  • A minimum of 6 years of proficiency in programming or scripting languages such as Python, Java, C++, Bash, PowerShell, or equivalent languages is required.
  • Experience with data pipeline and workflow management tools such as Apache Airflow, Kubeflow, managed orchestration services, or platform-native workflow tooling is required.
  • Proven experience leading AI infrastructure projects and teams, including technical direction, design reviews, delivery governance, and stakeholder alignment is required.
  • Strong project management, communication, problem-solving, and cross-functional collaboration skills in client or enterprise environments are required.
  • Demonstrated experience evaluating and selecting AI technologies, frameworks, reference architectures, and platform services for production solutions is required.
  • Expert-level hands-on architecture experience with Snowflake warehouses, databases, schemas, secure data sharing and access controls, Snowpark, Cortex and AI capabilities, Streams and Tasks, Streamlit, and cloud ecosystem integrations is required.
  • Deep knowledge of scalable data architecture, AI feature and data pipelines, model integration patterns, governance, query and warehouse optimization, observability, and resilience is required.
  • Strong experience with SQL, Python, dbt, Terraform, CI/CD, security guardrails, monitoring, and platform cost optimization is required.
  • Ability to evaluate Snowflake architecture options and produce standards, patterns, decision records, benchmarks, and executive-ready recommendations is required.
  • Experience applying MLOps, DataOps, or InfraOps practices for production data and AI pipelines, model enablement, monitoring, incident response, and rollback strategies is required.

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 architectural decisions across Snowflake warehouses, databases, schemas, secure data sharing and access controls, Snowpark, Streams and Tasks, Cortex and AI capabilities, Streamlit, and cloud integrations, aligning options with client standards and business objectives.
  • Architect and prototype cost-optimized AI-enabling data and feature pipelines, Snowpark workloads, model integration patterns, and AI application environments; build benchmarks, proofs of concept, and reusable implementation patterns.
  • Define architecture standards, reusable infrastructure-as-code patterns, CI/CD approaches, data and model enablement patterns, monitoring strategy, SLAs and SLOs, and cost and performance governance for production AI systems.
  • Lead architecture assessments and design reviews, validating 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 their place in enterprise solutions.
  • Provide executive- and client-level technical advisory, translating platform trade-offs into clear, defensible recommendations tied to business outcomes.
  • Mentor architects and Technical Architects, build community best practices, and represent the practice in internal and external technical forums.

Desired Qualifications

  • Snowflake certifications such as SnowPro Advanced Architect, SnowPro Advanced Data Architect, or related AI/data platform credentials are desirable.
  • 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 is desirable.
  • Exposure to Snowpark, Cortex/AI features, vector search, retrieval pipelines, feature engineering, model enablement, and AI application architecture is desirable.
  • Experience with data governance, secure data sharing, enterprise architecture roadmaps, vendor/partner management, FinOps, and production support operating models is desirable.
  • AI Agents and Workflow Integration is a good-to-have skill.

About the Company

Accenture

Advises businesses and runs their technology

Accenture advises large businesses and public agencies on strategy, builds their technology systems and runs ongoing operations. Its teams connect business planning with implementation, covering areas such as cloud migration, cybersecurity and customer experience. Consultants work with clients to redesign processes, while engineers integrate software from technology partners and maintain the resulting systems for multinational organizations. Clients pay for project work or continuing services, including application support and business processes such as finance and customer service. Accenture was founded in 1989 and has its headquarters in Dublin, Ireland.

Company size
10,001+
Company stage
IPO
Headquarters
Dublin, Ireland
Founded
1989

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