We are hiring for one of our clients, seeking a Dockerfile Data Validation Engineer to work on a full-time basis. The engineer will design, implement, and maintain data‑validation workflows inside Docker‑based build pipelines. The position supports cutting‑edge AI projects that require reliable, reproducible, and fully validated containerized data pipelines.Key Responsibilities:Develop and optimize Dockerfiles that embed data‑validation steps for datasets, schemas, and model artifacts.Implement label metadata standards to track dataset versions, schema definitions, and data lineage within container images.Create validation scripts a integrity verification, and quality‑control assessments.Integrate validation steps into CI/CD pipelines, enforcing fail‑on‑bad‑data policies to prevent non‑compliant releases.Document labeling conventions, validation logic, and data‑governance procedures for cross‑functional teams.Required Skills & Qualifications:Minimum four years of professional experience as a DevOps engineer with strong expertise in Docker and Dockerfile authoring.Proficiency in Python or Bash for building automated validation scripts and command‑line tools.Working knowledge of data formats, schema definition languages, and validation utilities commonly used in AI pipelines.Experience with CI/CD systems and container registries to automate build, test, and deployment workflows.Familiarity with data‑versioning concepts, MLOps practices, or tools such as Great Expectations is preferred.This role offers a unique opportunity to work with a global leader ormation and internet sector, contributing to the delivery of reliable AI systems for enterprise customers. The engineer will collaborate with data engineering, machine learning, and DevOps teams to ensure that data assets meet quality and compliance standards before they reach production.

Data Engineer (Data Validation) (Remote)

Emma

Responsibilities

Skills wanted:

Language(s) required:

Admin access needed