Data Engineer

At Mistral AI, we believe in the power of AI to simplify tasks, save time and enhance learning and creativity. Our technology is designed to integrate seamlessly into daily working life.

We democratize AI through high-performance, optimized, open-source and cutting-edge models, products and solutions. Our comprehensive AI platform is designed to meet enterprise needs, whether on-premises or in cloud environments. Our offerings include le Chat, the AI assistant for life and work.

We are a dynamic, collaborative team passionate about AI and its potential to transform society.

Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between France, USA, UK, Germany and Singapore. We are creative, low-ego and team-spirited.

Join us to be part of a pioneering company shaping the future of AI. Together, we can make a meaningful impact. See more about our culture on https://mistral.ai/careers .

Role Summary

We are seeking passionate and talented Data Engineers to join our team. Candidates with an Analytics Engineering background are also encouraged to apply.

What you will do

• Design, build, and maintain scalable data pipelines, ETL processes, and analytics infrastructure. Automate data quality checks and validation processes.
• Collaborate with cross-functional teams to understand data needs and deliver high-quality, actionable solutions, eg work closely with machine learning teams to support model training, deployment pipelines, and feature stores.
• Optimize data storage, retrieval, processing, and queries for performance, scalability, and cost-efficiency.
• Define and enforce data governance, metadata management, and data lineage standards.
• Ensure data integrity, security, and compliance with industry standards.

About You

• Master’s degree in Computer Science, Engineering, Statistics, or a related field.
• 3+ years of experience in data engineering, analytics engineering, or a related role.
• Proficiency in Python and SQL.
• Experience with dbt.
• Experience with cloud platforms (e.g., AWS, GCP, Azure) and data warehousing solutions (e.g., Snowflake, BigQuery, Redshift, Clickhouse).
• Strong analytical and problem-solving skills, with attention to detail.
• Ability to communicate complex data concepts to both technical and non-technical stakeholders.

Nice to Have

• Experience with machine learning pipelines, MLOps, and feature engineering.
• Knowledge of containerization and orchestration tools (e.g., Docker, Kubernetes).
• Familiarity with DevOps practices, CI/CD pipelines, and infrastructure-as-code (e.g., Terraform).
• Background in building self-service data platforms for analytics and AI use cases.

Hiring Process

• Intro call with Recruiter – 30 min
• Hiring Manager Interview – 30 min
• Technical interview – Live Coding (Python/SQL) – 45 min
• Technical interview – System Design – 45 min
• Value talk interview – 30 mins
• References

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