Data Architect with Snowflake & AI Platforms

Capgemini•New York, NY

1d•$90,786 - $110,762

About The Position

We are seeking a Data Architect with deep expertise in Snowflake-based data platforms and AI-driven analytics. This role will lead end-to-end data architecture, drive scalable and secure data solutions, and enable advanced analytics and AI/ML use cases leveraging Snowflake Cortex and modern data stack technologies. The ideal candidate will combine strong technical leadership, hands-on architecture skills, and the ability to collaborate with cross-functional business and technical teams.

Requirements

8+ years of experience in data architecture, data engineering, or data platform roles with at least 4 years specifically designing and optimizing solutions on Snowflake
Deep expertise in Snowflake features including warehouses, resource monitors, zero‑copy cloning, Time Travel, data sharing, Snowpark, Tasks/Streams, and security/governance controls
Strong proficiency in SQL, data modeling (conceptual, logical, physical), ETL/ELT patterns, and cloud data platforms (AWS, Azure, or GCP)
Proven experience designing secure, scalable architectures for analytics, reporting, and machine learning workloads
Solid understanding of data governance, quality, lineage, and compliance (e.g., GDPR, SOC2, HIPAA if applicable)
Bachelor’s or Master’s degree in computer science, Information Systems, Engineering, or a related field, or equivalent experience
Excellent communication, stakeholder management, and leadership skills

Nice To Haves

Hands‑on experience with Snowflake Cortex AI capabilities including Cortex Search, Cortex Analyst, LLM functions, vector embeddings, RAG patterns, and building AI agents or applications within Snowflake
SnowPro Core, Advanced, or Architect certifications
Experience with modern data stack tools such as dbt, Airflow, Kafka, Spark, Fivetran, Matillion, or similar
Knowledge of AI/ML workflows, vector databases, semantic search, and integrating structured and unstructured data for generative AI
Background in Data Vault 2.0, dimensional modeling, or domain‑driven design
Experience in regulated industries (finance, healthcare, pharma) or large‑scale enterprise environments is a plus

Responsibilities

Lead end‑to‑end data architecture and solution design for Snowflake‑based data platforms including logical/physical data models, ingestion patterns (batch, streaming, Snowpipe), storage layers (raw, curated, consumption/semantic), and consumption patterns for analytics, BI, and AI/ML
Design and optimize scalable, high‑performance data warehouses, data lakes, and lakehouse architectures on Snowflake focusing on performance tuning, query optimization, cost management, workload/warehouse strategies, and autoscaling
Architect and implement AI‑powered solutions using Snowflake Cortex including Cortex LLM functions, Cortex Search for semantic/vector/RAG capabilities, Cortex Analyst for conversational analytics, Document AI, and integration with external LLMs (e.g., for fine‑tuning, agents, and multimodal data processing)
Define and enforce data governance, security, and compliance frameworks including RBAC, row/column access policies, dynamic data masking, encryption, secure data sharing, and private listings
Design data pipelines integrating with various sources (on‑prem, cloud, SaaS) and orchestration tools; implement real‑time capabilities using Streams, Tasks, and Snowpark (Python/Scala/Java)
Collaborate with data engineers, analysts, scientists, and business stakeholders to deliver governed, reusable data products that accelerate analytics and AI initiatives
Provide technical leadership in migrations to Snowflake from legacy systems (e.g., on‑prem warehouses, other clouds) and establish reference architectures, patterns, and standards
Monitor platform health, optimize for cost/performance, and implement disaster recovery, replication, and high‑availability strategies
Mentor junior architects and engineers, conduct design reviews, and promote best practices in data modeling (e.g., Data Vault, Kimball, or hybrid semantic modeling) and AI‑ready data foundations

Benefits

Paid time off based on employee grade (A-F), defined by policy: Vacation: 12-25 days, depending on grade, Company paid holidays, Personal Days, Sick Leave
Medical, dental, and vision coverage (or provincial healthcare coordination in Canada)
Retirement savings plans (e.g., 401(k) in the U.S., RRSP in Canada)
Life and disability insurance
Employee assistance programs
Other benefits as provided by local policy and eligibility

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