Data Validation Engineer

ICF • Reston, AL

2d • $98,614 - $167,644 • Remote

About The Position

ICF's Digital Modernization Division is an information technology and management consulting organization that delivers integrated, strategic solutions to federal clients. We bring expertise in cloud, cybersecurity, enterprise architecture, data modernization, and digital transformation to support mission-critical government programs. Join a team accelerating the modernization of a large federal agency's enterprise data and analytics ecosystem. This cloud-based platform provides data storage, analytics, governance, and AI/ML capabilities that enable thousands of users to transform data into actionable insights. As demand continues to grow, the team is focused on migrating legacy workloads, streamlining onboarding and support, expanding platform capabilities, and helping organizations across the enterprise adopt modern data and AI solutions at scale. The Data Validation Engineer 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.

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.

Nice To Haves

• 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.

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.

Benefits

• Reasonable Accommodations are available, including, but not limited to, for disabled veterans, individuals with disabilities, and individuals with sincerely held religious beliefs, in all phases of the application and employment process.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level