Data Validation Engineer

About this role

Please note: This role is contingent upon a contract award. While it is not an immediate opening, we are actively conducting interviews and extending offers in anticipation of the award.

The Work
At ICF our 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 Engingeer 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.

Job Location: Remote, however, strong preference for candidates who live in the Washington DC Metro Area. There will be occasional onsite meetings on the client site in Washington, DC.

*If you accept this position, you should note that ICF does monitor employee work locations, blocks access from foreign locations/foreign IP addresses, and prohibits personal VPN connections.

What You Will Do:

• 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.
Basic Qualifications

• 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.

Preferred Qualifications

• 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…