Data Validation Engineer

ICF is hiring a Data Validation Engineer to support modernization of a federal agency’s enterprise data and analytics ecosystem. The role implements data quality and observability controls, monitors pipelines, and works with Governance to operationalize quality rules, thresholds, and publication gates.

Responsibilities:

• Implements technical controls for data parity, freshness, anomaly detection, pipeline observability, alerting, defect tracking, and release evidence
• Wires pipelines to monitoring and reporting mechanisms so data quality problems are detected and acted upon before customer release
• Works with Governance on rule libraries, Definitions of Done, quality thresholds, and publication gates, while ensuring the engineering team can operationalize those requirements efficiently
• Apply Data quality automation, parity testing, freshness SLAs, anomaly detection, pipeline observability, alerting, data lineage, and defect tracking to support role delivery
• Collaborate with relevant product, engineering, security, governance, quality, and customer-facing stakeholders as required by the role
• Document work products, decisions, risks, and delivery evidence to support traceability and continuous improvement

Requirements:

• U.S. Citizenship is required due to federal contract requirements
• Candidate must reside in the U.S., be authorized to work in the U.S., and all work must be performed in the U.S
• Candidate must have lived in the U.S. for three (3) full years out of the last five (5) years
• Bachelor's degree in Computer Science, Data Engineering, Data Quality Engineering, Information Systems, Statistics, Applied Mathematics, Software Engineering, or related field; or a high school diploma with four (4) additional years of relevant experience in lieu of a bachelor's degree
• Minimum 6 years of relevant experience aligned to the responsibilities of this role
• Master's degree may substitute for two (2) years of relevant experience
• Highly effective analytical, problem-solving, and decision-making capabilities
• Excellent written and verbal communication skills, with the ability to work effectively across technical and non-technical audiences
• Strong organization, attention to detail, and the ability to prioritize and manage multiple responsibilities
• Collaborative approach with a commitment to quality, accountability, and continuous improvement
• Experience designing and implementing automated data quality, data validation, and data observability frameworks within cloud-based data and analytics platforms
• Strong experience developing automated data quality controls, reconciliation processes, parity testing frameworks, and release validation mechanisms for large-scale data modernization and migration efforts
• Experience implementing data freshness monitoring, service-level agreements (SLAs), data certification workflows, and publication readiness controls
• Experience building anomaly detection, drift detection, statistical validation, and exception monitoring capabilities across structured, semi-structured, and analytical datasets
• Strong expertise with Databricks, Delta Lake, Delta Live Tables, SQL, Python, Spark, and modern Lakehouse architectures
• Experience implementing automated validation across medallion architecture layers (raw, bronze, silver, gold), data pipelines, data products, reporting layers, and published analytical assets
• Experience utilizing data quality and observability frameworks such as Great Expectations, Soda, Monte Carlo, Databricks Expectations, Deequ, or comparable technologies
• Experience monitoring and validating ETL/ELT pipelines, Azure Data Factory workflows, Spark jobs, Databricks Workflows, APIs, streaming pipelines, and enterprise integrations
• Experience with metadata management, data lineage, governance controls, and publication certification processes leveraging Unity Catalog, Collibra EDC, Microsoft Purview, or similar technologies
• Experience developing automated alerting, defect detection, operational dashboards, issue triage processes, and quality metrics using monitoring and reporting platforms
• Experience implementing DataOps practices, pipeline observability, operational telemetry, root-cause analysis, error classification, and automated remediation patterns
• Experience supporting AI/ML and analytics workloads through training-data validation, feature quality monitoring, model-input validation, model-output verification, drift monitoring, explainability assessments, and AI quality controls
• Familiarity with MLOps practices, MLflow, Azure Machine Learning, Databricks ML, model lifecycle management, and production AI governance
• Experience developing release evidence, validation reports, audit artifacts, quality scorecards, and engineering controls that support compliant and repeatable deployments
• Experience collaborating with Data Governance Leads, Data Quality Analysts, Data Engineers, AI Engineers, Product Owners, Architects, QA teams, and Platform Engineers to operationalize quality requirements and governance controls
• Experience implementing test automation and validation controls within CI/CD and DataOps pipelines utilizing GitHub Actions, Azure DevOps, Terraform, or equivalent automation platforms
• Strong understanding of data governance, data stewardship, lineage, metadata management, and publication approval processes
• Experience supporting Federal government, healthcare, or other highly regulated environments preferred
• Experience working in Agile, DevSecOps, DataOps, or cross-functional delivery teams