Dockerfile Data Validation Engineer

About this opportunity

Turing is seeking an engineer to design, implement, and maintain data-validation workflows inside Docker-based build pipelines. The role focuses on creating and managing Dockerfile labels, metadata standards, and validation scripts to ensure datasets, schemas, and model artifacts meet quality and compliance requirements before deployment. The engineer will collaborate with data engineering, machine learning, and DevOps teams to build reliable, reproducible, and fully validated containerized data pipelines.

What the listing describes

• ✓Develop and optimize Dockerfiles with built-in data-validation steps
• ✓Implement Docker LABEL metadata for dataset versions, schemas, and data lineage
• ✓Create Python or Bash validation scripts for schema checks, data integrity, and quality control
• ✓Integrate data-validation steps into CI/CD pipelines
• ✓Implement fail-on-bad-data checks within build and deployment workflows
• ✓Document standards for Dockerfile labeling and validation logic
• ✓Document data governance standards related to validation workflows
• ✓Collaborate with data engineering, machine learning, and DevOps teams
• ✓Build reliable and reproducible containerized data pipelines
• ✓Validate datasets, schemas, and model artifacts before deployment

Requirements shown on the source

• ✓4+ years of experience as a DevOps engineer
• ✓Strong experience with Docker and Dockerfiles
• ✓Proficiency in Python or Bash for validation scripting
• ✓Knowledge of data formats, schemas, and validation tools
• ✓Familiarity with CI/CD systems
• ✓Familiarity with container registries
• ✓Ability to work with data-validation and data-quality workflows
• ✓Ability to work in a fully remote environment
• ✓Availability for at least 4 hours per day and a minimum of 20 hours per week
• ✓Ability to provide 4 hours of overlap with PST

Relevant backgrounds mentioned

• ✓Previous participation in LLM research or evaluation projects
• ✓Experience building or testing developer tools
• ✓Experience building or testing automation agents
• ✓Experience with MLOps workflows
• ✓Experience with data versioning
• ✓Experience with Great Expectations
• ✓Knowledge of Kubernetes
• ✓Knowledge of container security tools