Senior Tester

Iconma Portal•McLean, VA

About The Position

The Senior Tester will support an enterprise data modeling transformation initiative for a real estate and financial services client. The contractor will independently assess the processes and technology ecosystem supporting data modeling, conduct structured manual testing of critical workflows, and develop a practical, phased plan for testing automation and quality gates. The role partners with data modelers, data engineers, platform teams, and data governance stakeholders to improve the quality, consistency, and release readiness of data modeling artifacts and related metadata. The work includes evaluating the end-to-end data modeling lifecycle, its tools and integrations, and alignment with enterprise standards. The contractor will identify gaps, risks, bottlenecks, and control weaknesses, document findings, and recommend priorities for improvement. They will also create and execute manual test plans and cases, document evidence and defects, support triage and retesting, and analyze results and report status and recommendations. The contractor will define a testing strategy for data modeling transformation outcomes and identify candidates for automation. Potential approaches include automated standards validation, model diffing and regression checks, CI/CD quality gates, model-to-implementation consistency verification, and metadata publishing checks. Deliverables include a current-state assessment, manual test documentation and results, a phased automation roadmap with dependencies, effort estimates, and success criteria, and recommended KPIs and controls. Proof-of-concept automation scripts or pipeline examples may be included if agreed in scope.

Requirements

At least 7 years of experience in data engineering, data architecture, data modeling, QA, or related roles with a strong testing focus.
At least 8 years of automation testing experience.
Data modeler or data modeling experience is required, with a strong understanding of logical data modeling.
Experience testing data, metadata, and data modeling workflows beyond application UI testing.
Strong knowledge of data modeling concepts, including entities and relationships, keys, normalization, dimensional and relational patterns, and naming and standards.
Demonstrated ability to develop test plans and test cases and execute structured manual testing with clear documentation.
Experience setting up testing and automation, including hands-on automation framework development using Java and/or Python.
Strong SQL and JSON validation skills.
Strong analytical, communication, and stakeholder-management skills, including the ability to produce actionable assessments and roadmap deliverables.
Experience with enterprise data modeling tools such as ER/Studio, ERwin, SAP PowerDesigner, Sparx EA, or similar.
Experience testing applications used to build and manage data models.
Familiarity with metadata, catalog, or governance platforms such as Collibra, Alation, Informatica, or Microsoft Purview.
Experience with CI/CD and automation tooling such as GitHub, GitLab, Azure DevOps, or Jenkins, and scripting; Python is preferred.
Experience implementing automated quality checks such as rules engines, schema validation, or model diffing.
Familiarity with enterprise data platforms such as Snowflake, Databricks, SQL Server, Oracle, or PostgreSQL, and with DDL deployment patterns.
Database experience with Snowflake and PostgreSQL.
Experience with all phases of testing, including system testing, SIT, and UAT.
Experience with Hewlett Packard ALM (Quality Center) version 11.0.
Experience with Microsoft Visio and Microsoft Office applications, including Word, Excel, and PowerPoint.
Advanced Excel skills, including complex worksheets.
Familiarity with agile methodology.
Mortgage industry and data experience.
Ability to understand business requirements.
AI/ML fluency or exposure.

Nice To Haves

Experience with Cucumber framework.
AI/ML exposure.
Optional ability to deliver proof-of-concept automation scripts or pipeline examples, subject to scope agreement.

Responsibilities

Assess end-to-end data modeling lifecycle processes, including intake, design, review and approval, governance, versioning, publication, change management, and release.
Evaluate data modeling tools and integrations, including metadata/catalog platforms, version control, CI/CD, and ticketing or work-management systems.
Identify and document process and technology gaps, risks, bottlenecks, and control weaknesses; provide prioritized recommendations.
Review alignment with enterprise standards for naming conventions, modeling patterns, domain boundaries, stewardship, metadata, and lineage.
Create and execute manual test plans and cases for model creation and updates; standards validation; model-to-DDL generation and deployment readiness; versioning, branching, merging, and promotion; metadata publishing; and role-based access controls.
Document test evidence, defects, and remediation recommendations; support defect triage and retesting.
Analyze test results, track and manage defects, report status, and recommend modifications to test plans or schedules.
Define a fit-for-purpose testing strategy for data modeling transformation outcomes, including quality, governance, velocity, and auditability.
Identify automation candidates and recommend what should be automated versus kept manual, including approaches and integration points.
Develop a phased automation roadmap with dependencies, effort estimates, measurable success criteria, and recommended KPIs and controls.
Partner with data modelers, data engineers, platform teams, governance stakeholders, and other delivery teams.
Optionally develop proof-of-concept automation scripts or pipeline examples if agreed in scope.