Senior Data Architect - Snowflake & AI

About the role

This role leads high-impact data programs for major enterprise clients. The position combines deep Snowflake and AI expertise with strategic consulting. Success depends on driving transformation and building client capability. You will define the technical vision for data platforms and ensure that architecture aligns with business outcomes. You will partner with stakeholders to uncover requirements and translate them into scalable data strategies. You will mentor team members and elevate the standard of data practices across engagements. You will act as a trusted advisor, guiding clients through complex data transformations and AI adoption. You will own the delivery of end-to-end data solutions that are robust, secure, and future-proof.

Key facts

What you'll do

Demonstrate expert-level mastery of Snowflake architecture, performance tuning, access controls, security, and Cortex AI features.

Design and govern robust data architectures that leverage modern data stack best practices and cloud-native patterns.

Lead the integration of disparate systems using tools such as dbt, Fivetran, Stitch, and Airflow or equivalent orchestration platforms.

Develop and implement data models that support analytics, reporting, and AI workloads across the enterprise.

Champion the adoption of reverse ETL platforms and CDP architectures to synchronize data across systems.

Utilize streaming data patterns and platforms to enable real-time insights and responsive data ecosystems.

Apply knowledge of agentic AI frameworks, prompt engineering, and AI-assisted data engineering to automate workflows.

Evaluate and incorporate data catalog and governance tools to ensure metadata management and compliance.

Write efficient SQL and Python scripts to solve complex data problems and support analytical use cases.

Contribute to the data practice by sharing methodologies, building reusable assets, and improving delivery standards.

Requirements

The posting states a bachelor's degree requirement. +10 years of experience in data engineering or data architecture is required, with at least 5 years in a client-facing consulting or advisory role. Expert-level mastery of Snowflake architecture, performance tuning, access controls, security, and Cortex AI features is mandatory. Strong experience with dbt, Fivetran/Stitch, Airflow, or equivalent orchestration tools is required. Deep knowledge of data modeling techniques and modern data stack best practices is required. Excellent communication skills in French and English (C1) are required to present architecture to executives and explain details to engineers. Demonstrated ability to build trust with clients and drive organisational change is required.

Nice to have qualifications

Hands-on experience with Snowflake Cortex AI features, including LLM Functions, Cortex Analyst, and Cortex Agents is valued. Knowledge of agentic AI frameworks, prompt engineering, and AI-assisted data engineering workflows is valued. Experience with reverse ETL platforms and CDP architectures is valued. Familiarity with streaming data platforms and real-time data patterns is valued. Cloud platforms experience across GCP, AWS, and Azure with their native data services is valued. Knowledge of data catalog and governance tools is valued. SQL fluency and Python scripting proficiency are valued. Preferred certifications include Snowflake SnowPro Core and Advanced/Architect certification, which is strongly preferred, and dbt Analytics Engineering certification is a plus.

Practical notes

Career advancement includes international mobility and professional development programs.

Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

The role involves experience innovation at a global scale. The work spans consulting, technical architecture, and AI-augmented engineering. Success requires curiosity, teamwork, and cross-industry pattern application.

Questions to ask

Worth asking in any interview: how the team measures success, who the role works with daily, what the onboarding looks like, and what the company is trying to achieve this year. Asking what past hires did well is a strong final question. Keep the list short and pick the questions that matter most to you.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.