Snowflake AI Data Engineer

Choosing Capgemini means choosing a company where you will be empowered to shape your career in the way you’d like, where you’ll be supported and inspired by a collaborative community of colleagues around the world, and where you’ll be able to reimagine what’s possible. Join us and help the world’s leading organizations unlock the value of technology and build a more sustainable, more inclusive world.

Job Description Role Overview

• The Snowflake AI Data Engineer / Lead serves as the onsite technical lead, driving Snowflake-native AI, GenAI, and advanced data engineering on AWS Cloud.
• This role requires deep expertise in Snowflake, Cortex AI, Snowpark (Python), and Talend ETL, with ownership of delivering AI/ML and LLM-powered solutions directly within the Snowflake ecosystem, while coordinating with offshore teams.

Key Responsibilities

• Design and implement Cortex-driven AI solutions using LLM capabilities (COMPLETE, CHAT, SUMMARIZE), embeddings (EMBED_TEXT), vector search, and RAG architectures.
• Enable AI copilots, semantic search, and intelligent analytics on enterprise data.

• Own Snowflake platform architecture, including:

• Schemas
• Views
• Streams
• Tasks

• Snowpipes

• Enforce performance optimization best practices:

• Warehouse sizing
• Clustering

• Query tuning

• Implement secure data access using:

• RBAC
• Masking
• Row-level controls

• Column-level controls

• Lead data engineering and integration pipelines (batch and near real-time) using Talend, Qlik Replicate, and Snowpipe.
• Ensure data quality, reconciliation, and schema evolution.
• Enable ingestion from diverse source systems.
• Guide SQL, Python, and ELT transformations.

• Enable AI/ML workflows using Snowpark and integration with platforms such as:

• Databricks
• SageMaker

• Azure ML

• Deliver AI-driven use cases such as:

• Claims triage
• Fraud detection
• Risk scoring
• Premium leakage

• Pricing analytics

• Leverage Snowflake-native AI capabilities and Python-based data science frameworks.
• Support CI/CD-driven deployments and automation.

Skills & Qualifications

• Strong experience in Snowflake, Cortex AI, Snowpark (Python), Talend ETL, and AWS Cloud.
• Hands-on expertise in LLMs, embeddings, vector search, and RAG architectures within Snowflake.
• Proven leadership in Snowflake architecture, performance optimization, and scalable data platforms.
• Deep experience in data engineering, ELT pipelines, and real-time/batch data integration.
• Experience enabling AI/ML workloads and feature engineering within Snowflake environments.
• Strong knowledge of data security frameworks (RBAC, masking, row/column-level controls).
• Domain experience in insurance or financial services.
• Advanced proficiency in SQL and Python, with the ability to lead engineering teams.

The base compensation range for this role in the posted location is: 72,000 to 101,050

Capgemini provides compensation range information in accordance with applicable national, state, provincial, and local pay transparency laws. The base compensation range listed for this position reflects the minimum and maximum target compensation Capgemini, in good faith, believes it may pay for the role at the time of this posting. This range may be subject to change as permitted by law.

The actual compensation offered to any candidate may fall outside of the posted range and will be determined based on multiple factors legally permitted in the applicable jurisdiction.

These may include, but are not limited to: Geographic location, Education and qualifications, Certifications and licenses, Relevant experience and skills, Seniority and performance, Market and business consideration, Internal pay equity.

It is not typical for candidates to be hired at or near the top of the posted compensation range.

In addition to base salary, this role may be eligible for additional compensation such as variable incentives, bonuses, or commissions, depending on the position and applicable laws.

Benefits Capgemini offers a comprehensive, non-negotiable benefits package to all regular, full-time employees. In the U.S. and Canada, available benefits are determined by local policy and eligibility and may include:

• 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 healt…