Data Engineer (AI)

TL;DR

Data Engineer (Python/SQL): Designing and building data pipelines from scratch to handle petabytes of logs and model traces with an accent on scalability, reliability, and data quality. Focus on building automated scraping systems, transforming raw data into structured datasets, and developing internal data products.

Location: Hybrid in Paris, France (relocation package available)

Salary: $60k – $80k + Equity

Company

AI Safety company building the safety, reliability, and optimization layer for AI systems.

What you will do

• Design, build, and evolve internal data pipelines and tooling from scratch.
• Implement web scraping to convert raw external sources into structured, reliable datasets.
• Maintain and version data pipelines to ensure high quality through monitoring and testing.
• Develop new data products and analytics dashboards to meet business needs.
• Diagnose and resolve pipeline issues to ensure system robustness.
• Collaborate on infrastructure tasks to improve overall system stability.

Requirements

• Proficiency in Python and SQL for handling complex, real-world data.
• Experience with web scraping and building resilient, monitored pipelines.
• Strong knowledge of PostgreSQL or similar relational databases.
• Experience with cloud data warehouses (e.g., ClickHouse, BigQuery, Redshift, Snowflake).
• English: Proficiency required as the primary company language.

Nice to have

• Experience with Metabase or other BI tools.
• Knowledge of dbt for data modeling and analytics engineering.
• Familiarity with ETL tools such as Fivetran, Airbyte, or dltHub.
• Experience with AWS services (Athena, Glue, S3).
• Experience with AI-assisted or agentic coding.

Culture & Benefits

• Competitive medical insurance in France.
• Relocation package for moving to Paris.
• Paid time off according to local regulations.
• Provision of all necessary hardware, AI agent subscriptions, and IDEs.
• Bi-annual team off-sites.

Hiring process

• Introductory call with a team member.
• Technical take-home exercise.
• Technical interview.
• Final call with the CEO and CTO.