Data Validation Engineer (AI)

Location: Remote within the United States; candidates must reside in the U.S. and perform all work in the U.S. Strong preference for the Washington, DC Metro Area. Occasional onsite meetings are required at the client site in Washington, DC. Access from foreign locations and personal VPN connections are prohibited.

Salary: $98,614–$167,644 annually.

Company

hirify.global is an advisory and technology services provider delivering cloud, cybersecurity, enterprise architecture, data modernization, and digital transformation solutions to federal clients.

What you will do

• Implement data parity, freshness, anomaly detection, observability, alerting, defect tracking, and release-evidence controls.
• Connect data pipelines to monitoring and reporting systems so quality issues are detected before customer release.
• Partner with governance, engineering, security, quality, product, and customer-facing stakeholders to operationalize quality requirements.
• Develop validation automation across data pipelines, data products, reporting layers, and published analytical assets.
• Document decisions, risks, work products, and delivery evidence for traceability and continuous improvement.

Requirements

• U.S. citizenship, U.S. residence, U.S. work authorization, and three full years lived in the U.S. during the last five years are required.
• Bachelor’s degree in computer science, data engineering, data quality engineering, information systems, statistics, applied mathematics, software engineering, or a related field; a high school diploma plus four additional years of relevant experience may substitute.
• At least six years of relevant experience; a master’s degree may substitute for two years of experience.
• Experience with automated data quality, validation, observability, reconciliation, parity testing, freshness monitoring, anomaly detection, and release validation.
• Strong expertise with Databricks, Delta Lake, Delta Live Tables, SQL, Python, Spark, and modern lakehouse architectures.
• Experience with data governance, lineage, metadata management, CI/CD or DataOps automation, and regulated or federal environments.

Nice to have

• Experience with Great Expectations, Soda, Monte Carlo, Deequ, Databricks Expectations, Unity Catalog, Collibra EDC, or Microsoft Purview.
• Experience supporting AI/ML workloads, MLOps, MLflow, Azure Machine Learning, Databricks ML, model validation, and drift monitoring.
• Experience with Azure Data Factory, Databricks Workflows, APIs, streaming pipelines, GitHub Actions, Azure DevOps, or Terraform.
• Experience working in Agile, DevSecOps, DataOps, or cross-functional delivery teams.

Culture & Benefits

• Full-time employment supporting a large federal agency’s cloud-based data and analytics platform.
• Work is primarily remote within the United States, with occasional client-site meetings in Washington, DC.
• Collaboration across data engineering, AI engineering, governance, architecture, QA, platform, security, and product functions.
• Focus on quality, accountability, traceability, compliant deployments, and continuous improvement.

Hiring process

• The role is contingent upon a contract award.
• Interviews and offers are being conducted in anticipation of the award.