Remote | Contractor | 2–4 Week Assignment
20, 30, or 40 Hours/Week | 4-Hour PST Overlap Required
Eligible African Locations: Nigeria, Kenya, Egypt, Ghana
About The Role
We are seeking an experienced
Dockerfile Data Validation Engineer to design, implement, and maintain
data-validation workflows inside Docker-based build pipelines.
In this role, you will build and manage
Dockerfile labels, metadata standards, and automated validation scripts to ensure datasets, schemas, and model artifacts meet quality and compliance requirements before deployment.
You will work closely with
data engineering, machine learning, and DevOps teams to create reliable, reproducible, and fully validated containerized data pipelines.
What You’ll Do
- Develop and optimize Dockerfiles with built-in data-validation steps.
- Implement Dockerfile LABEL metadata for dataset versions, schemas, and lineage.
- Create Python and/or Bash validation scripts for:
- Schema validation
- Data integrity checks
- Data quality control
- Integrate data-validation steps into CI/CD pipelines.
- Implement and enforce fail-on-bad-data checks to prevent invalid data from progressing through the pipeline.
- Establish and maintain standards for Dockerfile labeling.
- Document validation logic, metadata standards, and data-governance requirements.
- Help ensure datasets, schemas, and model artifacts are properly validated before deployment.
- Work with data engineering, machine learning, and DevOps teams to maintain reliable and reproducible containerized workflows.
Required Qualifications
- 4+ years of DevOps engineering experience.
- Strong hands-on experience with Docker and Dockerfiles.
- Proficiency in Python or Bash for validation scripting.
- Knowledge of data formats, schemas, and data-validation tools.
- Familiarity with CI/CD systems.
- Experience working with container registries.
Nice to Have
- Previous participation in LLM research or evaluation projects.
- Experience building or testing developer tools or automation agents.
- Experience with MLOps workflows.
- Experience with data versioning.
- Experience with Great Expectations.
- Knowledge of Kubernetes.
- Knowledge of container security tools.
Core Technical Skills
- Python
- Bash
- Docker
- Dockerfiles
- CI/CD
- Data Validation
- Data Schemas
- Data Integrity
- Data Quality
- Container Registries
Project Details
- Employment Type: Contractor assignment
- Contract Duration: 2–4 weeks
- Expected Start: Next week
- Work Arrangement: Fully remote
- Time Commitment Options: 20, 30, or 40 hours per week
- Minimum Daily Commitment: 4 hours per day
- Required Overlap: 4 hours with PST
- Benefits: No medical or paid leave
Eligible African Countries
Evaluation Process
The evaluation process takes approximately
75 minutes.
- Technical Interview: 30–60 minute technical discussion conducted in QODE.
What Success Looks Like
Success in this role means building a
reliable, automated, and reproducible data-validation layer within Docker-based build pipelines.
You Will Be Expected To Ensure That
- Dockerfiles incorporate effective validation workflows.
- Dataset versions, schemas, and lineage are properly represented through metadata.
- Python/Bash validation scripts accurately identify data-quality and integrity issues.
- CI/CD pipelines can automatically fail when data does not meet validation requirements.
- Validation processes are consistent and reproducible.
- Data-validation standards and logic are clearly documented.
- Data, ML, and DevOps teams can rely on the resulting workflows to identify problems before deployment.
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