{"id":2099517,"url":"https://alion.io/job/icf-data-validation-engineer","title":"Data Validation Engineer","company":{"id":2632,"name":"ICF","domain":"icf.com","url":"https://alion.io/company/icf","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":100,"open_postings":17,"ghost_share":0,"stale_share":0,"repost_share":0.059,"time_to_fill_p50_days":21,"computed_at":"2026-10-10T05:45:15Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":["Reston, United States","Charlotte, United States","United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":98614,"max":167644,"currency":"USD","period":"year","gross":null,"usd_annual":167644},"salary_estimate":null,"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anomaly Detection","optional":false},{"name":"VPN","optional":false},{"name":"Agile","optional":true},{"name":"Azure","optional":true},{"name":"Azure Data Factory","optional":true},{"name":"Azure DevOps","optional":true},{"name":"CI/CD","optional":true},{"name":"Collibra","optional":true},{"name":"Databricks","optional":true},{"name":"Delta Lake","optional":true},{"name":"ETL/ELT","optional":true},{"name":"GitHub Actions","optional":true},{"name":"Great Expectations","optional":true},{"name":"Machine Learning","optional":true},{"name":"MLFlow","optional":true},{"name":"Python","optional":true},{"name":"Spark","optional":true},{"name":"SQL","optional":true},{"name":"Terraform","optional":true}],"status":"live","first_seen_at":"2026-10-08T17:52:20Z","employer_posted_date":"2026-10-08","last_verified_at":"2026-10-10T23:27:42Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"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.\nThe Work\nAt 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.\nJoin 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.\nThe 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.\nJob 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.\n*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.\nWhat You Will Do:\nImplements technical controls for data parity, freshness, anomaly detection, pipeline observability, alerting, defect tracking, and release evidence. \nWires pipelines to monitoring and reporting mechanisms so data quality problems are detected and acted upon before customer release. \nWorks with Governance on rule libraries, Definitions of Done, quality thresholds, and publication gates, while ensuring the engineering team can operationalize those requirements efficiently. \nApply Data quality automation, parity testing, freshness SLAs, anomaly detection, pipeline observability, alerting, data lineage, and defect tracking to support role delivery. \nCollaborate with relevant product, engineering, security, governance, quality, and customer-facing stakeholders as required by the role. \nDocument work products, decisions, risks, and delivery evidence to support traceability and continuous improvement.\nBasic Qualifications\nU.S. Citizenship is required due to federal contract requirements. \nCandidate must reside in the U.S., be authorized to work in the U.S., and all work must be performed in the U.S. \nCandidate must have lived in the U.S. for three (3) full years out of the last five (5) years. \nBachelor'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. \nMinimum 6 years of relevant experience aligned to the responsibilities of this role. \nMaster's degree may substitute for two (2) years of relevant experience. \nPreferred Qualifications\nExperience designing and implementing automated data quality, data validation, and data observability frameworks within cloud-based data and analytics platforms. \nStrong experience developing automated data quality controls, reconciliation processes, parity testing frameworks, and release validation mechanisms for large-scale data modernization and migration efforts. \nExperience implementing data freshness monitoring, service-level agreements (SLAs), data certification workflows, and publication readiness controls. \nExperience building anomaly detection, drift detection, statistical validation, and exception monitoring capabilities across structured, semi-structured, and analytical datasets. \nStrong expertise with Databricks, Delta Lake, Delta Live Tables, SQL, Python, Spark, and modern Lakehouse architectures. \nExperience implementing automated validation across medallion architecture layers (raw, bronze, silver, gold), data pipelines, data products, reporting layers, and published analytical assets. \nExperience utilizing data quality and observability frameworks such as Great Expectations, Soda, Monte Carlo, Databricks Expectations, Deequ, or comparable technologies. \nExperience monitoring and validating ETL/ELT pipelines, Azure Data Factory workflows, Spark jobs, Databricks Workflows, APIs, streaming pipelines, and enterprise integrations. \nExperience with metadata management, data lineage, governance controls, and publication certification processes leveraging Unity Catalog, Collibra EDC, Microsoft Purview, or similar technologies. \nExperience developing automated alerting, defect detection, operational dashboards, issue triage processes, and quality metrics using monitoring and reporting platforms. \nExperience implementing DataOps practices, pipeline observability, operational telemetry, root-cause analysis, error classification, and automated remediation patterns. \nExperience 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. \nFamiliarity with MLOps practices, MLflow, Azure Machine Learning, Databricks ML, model lifecycle management, and production AI governance. \nExperience developing release evidence, validation reports, audit artifacts, quality scorecards, and engineering controls that support compliant and repeatable deployments. \nExperience 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. \nExperience implementing test automation and validation controls within CI/CD and DataOps pipelines utilizing GitHub Actions, Azure DevOps, Terraform, or equivalent automation platforms. \nStrong understanding of data governance, data stewardship, lineage, metadata management, and publication approval processes. \nExperience supporting Federal government, healthcare, or other highly regulated environments preferred. \nExperience working in Agile, DevSecOps, DataOps, or cross-functional delivery teams. \nProfessional Skills:\nHighly effective analytical, problem-solving, and decision-making capabilities. \nExcellent written and verbal communication skills, with the ability to work effectively across technical and non-technical audiences. \nStrong organization, attention to detail, and the ability to prioritize and manage multiple responsibilities. \nCollaborative approach with a commitment to quality, accountability, and continuous improvement. \n#LI-CC1\n#Indeed\nBot and Third-Party Applications\nPlease note that this application must be submitted directly by the applicant for consideration. Failure to do so may result in the application being excluded for consideration. Applicants needing an accommodation for disability or religious purposes in connection with the application process should contact  for assistance.\nWorking at ICF\nICF is a global advisory and technology services provider, but we’re not your typical consultants. We combine unmatched expertise with cutting-edge technology to help clients solve their most complex challenges, navigate change, and shape the future.We can only solve the world's toughest challenges by building a workplace that allows everyone to thrive. We are an equal opportunity employer.Together, our employees are empowered to share theirexpertiseand collaborate with others to achieve personal and professional goals. For more information, please read our EEO policy.\nWe will consider for employment qualified applicants with arrest and conviction records.\nReasonable Accommodations are available, including, but not limited to, for disabled veterans, individuals with disabilities, and individuals withsincerely heldreligious beliefs, in all phases of the application and employment process. To requestan accommodation,please email  and we will be happy toassist. All information you provide will be kept confidential and will be used only to the extentrequiredto provide needed reasonable accommodations.\nRead more about workplace discrimination rights or our benefit offerings which are included in the Transparency in (Benefits) CoverageAct.\nCandidate AI Usage Policy\nAt ICF, we are committed to ensuring a fair interview process for all candidates based on their own skills and knowledge. As part of this commitment, the use of artificial intelligence (AI) tools to generate orassistwith responses during interviews (whether in-person or virtual) is notpermitted. This policy is in place tomaintainthe integrity and authenticity of the interview process.\nHowever, we understand that some candidates may require accommodationthat involves the use of AI. Ifsuch anaccommodation is needed, candidates are instructed to contact us in advance at . Weare dedicated to providingthe necessary support to ensure that all candidates have an equal opportunity to succeed.\nPay Range - There are multiple factors that are considered in determining final payfor a position, including, but not limited to, relevant work experience, skills, certifications and competencies that align to the specified role, geographic location, education and certifications as well as contract provisions regarding labor categories that are specific to the position.\nThe pay range for this position based on full-time employment is:\n$98,614.00 - $167,644.00Nationwide Remote Office (US99)","description_format":"text","description_chars":10229,"description_truncated":false,"requirements":{"experience_years_min":6,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"high_school","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Energy Efficiency","Digital Government","Management Consulting"],"lifecycle":[{"event":"open","at":"2026-10-08T17:52:20Z"}],"visa":[],"liveness":{"score":90,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.903,"p_room":1,"age_days":1,"expected_fill_days":21,"reasons":["conf:3","velocity","win:early","comp:brand"],"computed_at":"2026-10-10T05:45:15Z"},"pay":{"stated_usd_annual":167644,"is_top_pay":true},"html_url":"https://alion.io/job/icf-data-validation-engineer","json_url":"https://alion.io/job/icf-data-validation-engineer.json","meta":{"generated_at":"2026-10-11T01:40:04Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":2451,"day_limit":5000,"remaining_today":2549,"minute_limit":60,"resets_at":"2026-10-12T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":2632},"rest":"https://alion.io/mcp/rest/get_company?id=2632"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Ficf-data-validation-engineer"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Ficf-data-validation-engineer"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Ficf-data-validation-engineer"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/icf-data-validation-engineer\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Ficf-data-validation-engineer"}]}