Mclean, VA Contract Sep 2, 2026 Tester Senior

• Client is seeking an experienced contractor to support an enterprise data modeling transformation initiative.

The contractor will perform an independent review of the current process and technology ecosystem supporting data modeling, execute structured manual testing of critical workflows, and develop a practical, phased plan to automate testing and quality gates. This role will partner with data modelers, data engineers, platform teams, and data governance stakeholders to improve quality, consistency, and release readiness of data modeling artifacts and related metadata. Can understand business requirements Write and execute test cases manually or using automation Analyze results of tests, defects tracking and management, report status and recommendations for modifications to test plan and/or schedule. Familiar with agile methodology Experience with:

• All phases of testing-system testing, SIT and UAT
• Hewlett Packard's ALM (Quality Center) version 11.0 testing tools
• Microsoft Visio
• Microsoft Office (Word, Excel, PowerPoint)
• SQL Key Responsibilities Process & Technology Review
• Assess end-to-end data modeling lifecycle processes (intake, design, review/approval, governance, versioning, publication, change management, and release).
• Evaluate tooling and integrations (data modeling tools, metadata/catalog, version control, CI/CD, ticketing/work management).
• Identify gaps, risks, bottlenecks, and control weaknesses; document findings and prioritized recommendations.
• Review alignment to enterprise standards (naming conventions, modeling patterns, domain boundaries, stewardship, metadata/lineage expectations). Manual Testing
• Create and execute manual test plans and test cases for key workflows, including:
• Model creation/updates (conceptual/logical/physical as applicable)
• Standards validation (naming, datatypes, keys, relationships, referential integrity)
• Model
• to-DDL generation and deployment readiness checks
• Versioning/branching/merging and promotion processes
• Metadata publishing and verification (catalog/glossary/lineage where applicable)
• Security and role-based access controls within tools
• Document test evidence, defects, and remediation recommendations; support triage and retesting. Test Strategy & Automation Roadmap
• Define a fit-for-purpose testing strategy for data modeling transformation outcomes (quality, governance, velocity, auditability).
• Identify automation candidates and define what should be automated vs. remain manual.
• Recommend an automation approach and integration points, potentially including:
• Automated standards checks (rule-based validation / linting)
• Model diffing and regression checks across versions
• CI/CD quality gates for model changes (PR checks, approvals, artifact packaging)
• Automated verification of model-to-implementation consistency (where feasible)
• Automated metadata publishing completeness checks
• Deliver a phased roadmap with dependencies, effort estimates, and measurable success criteria; optionally deliver a proof of concept if in scope.

Required Qualifications 7+ years of experience in data engineering, data architecture, data modeling, QA, or related roles with a strong testing focus. Demonstrated experience testing data/metadata/modeling workflows (beyond application UI testing). Strong knowledge of data modeling concepts: entities/relationships, keys, normalization, dimensional vs. relational patterns, naming/standards. Proven ability to develop test plans, write test cases, and execute structured manual testing with clear documentation. Strong analytical and communication skills; able to produce actionable assessment and roadmap deliverables. Preferred Qualifications Experience with enterprise data modeling tools (e.g., ER/Studio, ERwin, SAP PowerDesigner, Sparx EA, or similar). Familiarity with metadata/catalog/governance platforms (e.g., Collibra, Alation, Informatica, Microsoft Purview). Experience with CI/CD and automation tooling (e.g., GitHub/GitLab, Azure DevOps, Jenkins) and scripting (Python preferred). Experience implementing automated quality checks (rules engines, schema validation, model diffing). Familiarity with common enterprise data platforms (e.g., Snowflake, Databricks, SQL Server, Oracle, PostgreSQL) and DDL deployment patterns. Key Competencies Process analysis and continuous improvement Manual testing discipline and defect management Test strategy development and automation planning Data governance and standards enforcement Stakeholder management across architecture, engineering, governance, and delivery teams Deliverables (Expected Outputs) Current-state process and technology assessment with prioritized recommendations. Manual test plan, test cases, execution results, and defect log. Future-state testing strategy and test automation roadmap (phased). Recommended KPIs/controls (e.g., standards compliance rate, defect leakage, cycle time, automation coverage).

Optional:

proof-of-concept automation scripts/pipeline examples (if agreed in scope).