{"id":202448,"url":"https://alion.io/job/freshtalent-dockerfile-data-validation-engineer","title":"Dockerfile Data Validation Engineer","company":{"id":100173,"name":"FreshTalent","domain":"freshtalent.africa","url":"https://alion.io/company/freshtalent","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":true,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"middle","employment_type":"contractor","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"inferred_payroll_markers","remote_working_hours":{"label":"PST","utc_offset_min":-8,"utc_offset_max":-8},"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":["AO"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":70000,"max_usd":165000,"period":"year","method":"global_role_seniority_cell","sample_n":1015},"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"Great Expectations","optional":true},{"name":"Kubernetes","optional":true},{"name":"LLM","optional":true}],"status":"live","first_seen_at":"2026-09-03T12:27:53Z","employer_posted_date":null,"last_verified_at":"2026-09-03T12:27:53Z","board_verified":false,"closed_at":null,"days_open":56,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":56},"description":"Dockerfile Data Validation Engineer\nRemote | Contractor | 2-4 Week Assignment\n20, 30, or 40 Hours/Week | 4-Hour PST Overlap Required\nEligible African Locations: Nigeria, Kenya, Egypt, Ghana\nAbout the Role\nWe are seeking an experienced Dockerfile Data Validation Engineer to design, implement, and maintain data-validation workflows inside Docker-based build pipelines.\nIn 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.\nYou will work closely with data engineering, machine learning, and DevOps teams to create reliable, reproducible, and fully validated containerized data pipelines.\nWhat You’ll Do\nDevelop and optimize Dockerfiles with built-in data-validation steps.\nImplement Dockerfile LABEL metadata for dataset versions, schemas, and lineage.\nCreate Python and/or Bash validation scripts for: Schema validation\nData integrity checks\nData quality control\nIntegrate data-validation steps into CI/CD pipelines.\nImplement and enforce fail-on-bad-data checks to prevent invalid data from progressing through the pipeline.\nEstablish and maintain standards for Dockerfile labeling.\nDocument validation logic, metadata standards, and data-governance requirements.\nHelp ensure datasets, schemas, and model artifacts are properly validated before deployment.\nWork with data engineering, machine learning, and DevOps teams to maintain reliable and reproducible containerized workflows.\nRequired Qualifications\n4+ years of DevOps engineering experience.\nStrong hands-on experience with Docker and Dockerfiles.\nProficiency in Python or Bash for validation scripting.\nKnowledge of data formats, schemas, and data-validation tools.\nFamiliarity with CI/CD systems.\nExperience working with container registries.\nNice to Have\nPrevious participation in LLM research or evaluation projects.\nExperience building or testing developer tools or automation agents.\nExperience with MLOps workflows.\nExperience with data versioning.\nExperience with Great Expectations.\nKnowledge of Kubernetes.\nKnowledge of container security tools.\nCore Technical Skills\nPython\nBash\nDocker\nDockerfiles\nCI/CD\nData Validation\nData Schemas\nData Integrity\nData Quality\nContainer Registries\nProject Details\nEmployment Type: Contractor assignment\nContract Duration: 2-4 weeks\nExpected Start: Next week\nWork Arrangement: Fully remote\nTime Commitment Options: 20, 30, or 40 hours per week\nMinimum Daily Commitment: 4 hours per day\nRequired Overlap: 4 hours with PST\nBenefits: No medical or paid leave\nEligible African Countries\nNigeria\nKenya\nEgypt\nGhana\nEvaluation Process\nThe evaluation process takes approximately 75 minutes.\nTechnical Interview: 30-60 minute technical discussion conducted in QODE.\nWhat Success Looks Like\nSuccess in this role means building a reliable, automated, and reproducible data-validation layer within Docker-based build pipelines.\nYou will be expected to ensure that:\nDockerfiles incorporate effective validation workflows.\nDataset versions, schemas, and lineage are properly represented through metadata.\nPython/Bash validation scripts accurately identify data-quality and integrity issues.\nCI/CD pipelines can automatically fail when data does not meet validation requirements.\nValidation processes are consistent and reproducible.\nData-validation standards and logic are clearly documented.\nData, ML, and DevOps teams can rely on the resulting workflows to identify problems 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