Dockerfile Data Validation Engineer
FreshTalent →Dockerfile Data Validation Engineer at FreshTalent is a contract role based in Remote · Angola (remote). The listing asks for 4–15 yrs of experience. Listed skills: Python, Bash, Docker, Dockerfile, CI/CD and Data Validation. It was published on 3 September 2026 and was open at last check.
| Role | Dockerfile Data Validation Engineer |
|---|---|
| Company | FreshTalent |
| Location | Remote · Angola (remote) |
| Employment type | Contract |
| Experience asked | 4–15 yrs |
| Skills listed | Python, Bash, Docker, Dockerfile, CI/CD and Data Validation |
| Published | 3 September 2026 |
| Status | Open at last check |
Dockerfile Data Validation Engineer
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
LABELmetadata 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
- Nigeria
- Kenya
- Egypt
- Ghana
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.
Create a free OnJob profile to apply and see your AI match score before you apply. · Posted 3 Sept 2026.
Dockerfile Data Validation Engineer at FreshTalent — questions answered
What does the Dockerfile Data Validation Engineer role at FreshTalent pay?
FreshTalent does not publish a salary on this Dockerfile Data Validation Engineer listing, so OnJob shows no figure for it rather than an estimate. For what this role pays across the market, the OnJob salary guides aggregate the live listings that do disclose pay.
Is the Dockerfile Data Validation Engineer role at FreshTalent remote?
Yes — FreshTalent advertises this Dockerfile Data Validation Engineer role as remote, tied to Remote · Angola, and it is listed as contract work. Remote terms come from the employer's own posting, so confirm the expected working hours, timezone and any on-site days with FreshTalent before you apply.
What skills does the Dockerfile Data Validation Engineer role at FreshTalent require?
The Dockerfile Data Validation Engineer at FreshTalent listing names Python, Bash, Docker, Dockerfile, CI/CD and Data Validation. Those are the skills the employer put on the posting itself, so they are the ones worth matching in your profile and covering first in an interview.
How much experience do you need for the Dockerfile Data Validation Engineer role at FreshTalent?
FreshTalent asks for 4–15 yrs of experience on this Dockerfile Data Validation Engineer posting, alongside Python, Bash and Docker. Employers commonly consider candidates slightly under a stated band when the listed skills line up.
Is the Dockerfile Data Validation Engineer role at FreshTalent still open?
The Dockerfile Data Validation Engineer posting at FreshTalent was open at OnJob's last check of the employer's careers page, having been published on 3 September 2026. OnJob re-checks source listings on each build and marks a role closed once it disappears, but listings can close without notice, so the employer's own page is the final word.
How do you apply for the Dockerfile Data Validation Engineer role at FreshTalent?
Apply to the Dockerfile Data Validation Engineer at FreshTalent role through OnJob with a free profile: OnJob scores your fit against the listing, shows the skills lowering that score, and submits an ATS-ready profile. Creating a profile is free and needs no card.
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