Senior Data Engineer — Production AI Pipelines & ML Infra

Cavendish Professionals; Amsterdam; On-site; EUR 90,000 - 130,000; Full time

Job summary

Cavendish Professionals is seeking a Senior Data Engineer to design, build, and operate reliable ETL/ELT data pipelines using Python for AI-powered products in production. You will develop data infrastructure, work with graph databases and vector stores like SurrealDB, and manage cloud deployments with security controls.

The role emphasizes growing into technical leadership and collaborating across teams. You should bring 6–10 years of data engineering/ML experience and a strong track record

Qualifications

• 6-10 years' experience in data engineering, ML systems or backend-heavy engineering.
• Strong production Python experience building and maintaining data pipelines.
• Experience supporting ML or AI-powered products in production.
• Understanding or experience with graph databases and vector stores.
• Experience with cloud platforms, ideally GCP, plus DevOps, IaC and CI/CD.
• Familiarity with IAM, Secret Manager and SOC 2 requirements.

Responsibilities

• Design, build, and operate reliable ETL/ELT pipelines using Python.
• Develop and maintain data infrastructure supporting ML and AI-powered products.
• Work with graph databases and vector stores, including SurrealDB.
• Manage cloud infrastructure, deployment workflows and security controls.
• Help shape engineering practices as the team grows.

Skills

Python

Data pipelines

ML systems

Graph databases

Vector stores

DevOps

CI/CD

IAM practices

SOC 2 awareness

Tools

SurrealDB

GCP

Secret Manager

CI/CD tools

IAM

SOC 2

Job description

Cavendish Professionals is seeking a Senior Data Engineer to design, build, and operate reliable ETL/ELT data pipelines using Python for AI-powered products in production. You will develop data infrastructure, work with graph databases and vector stores like SurrealDB, and manage cloud deployments with security controls.

The role emphasizes growing into technical leadership and collaborating across teams. You should bring 6–10 years of data engineering/ML experience and a strong track record